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The Georgetown-IBM Experiment Demonstrated in January 1954
Abstract
The public demonstration of a Russian-English machine translation system in New York in January 1954 - a collaboration of IBM and Georgetown University - caused a great deal of public interest and much controversy. Although a small-scale experiment of just 250 words and six 'grammar' rules it raised expectations of automatic systems capable of high quality translation in the near future. This paper describes the system, its background, its impact and its implications.
... Ardından iki doğal dilin birbirine çevrildiği ilk bilgisayar programları gelmiştir. Yapılan ilk çeviri de Rusçadan İngilizceye yapılan 250 kelimelik bir çeviri olmuştur (Hutchins, 2004). Bu ilk denemeden sonra MÇ konusundaki çalışmalar inişli çıkışlı çeşitli dönemler yaşasa da günümüze kadar gelişerek gelmiş ve bugün ücretsiz servisler olarak elimizdeki cep telefonlarında bulunacak kadar yaygınlaşmışlardır. ...
... Bilgisayar bilimi alanında çalışan araştırmacılar insan dillerini modelleyerek programlama dillerini yaratmışlardır. Bundan önce ise dilin matematiksel yapısı modellenerek ilk bilgisayarların icadından bu yana MÇ bilgisayarların temel amaçlarından biri olmuştur (Hutchins, 2004). Bilgisayarlar ve çeviri konusu söz konusu olduğunda birbirine benzeyen ve kimi zaman birbirinin yerine kullanılan terimler mevcuttur. ...
... Deneyde 250 kelimelik bir Rusça metin İngilizceye tercüme edilmiştir Soğuk Savaş Dönemi'nin bu ilk yıllarında MÇ çalışmalarının Sovyetler Birliğinin resmi dili olan Rusçaya odaklanması anlaşılabilirdir. İlginç olan nokta deneyden sorumlu olan ve daha önce hem Başkan Roosvelt'in çevirmenliğini hem de BM ve Nünberg Mahkemelerindeki çeviri faaliyetlerinin organizasyonundan sorumlu olan Leon Dostert'in "birkaç yıl içerisinde MÇ'nin anlam belirsizliği problemini de aşarak makul çeviriler yapmaya elverişli hale geleceğini" duyurmasıdır (Hutchins, 2004). Ancak bu seviyeye ulaşılıp ulaşılamayacağı konusunda hala tartışmalar devam etmektedir. ...
Yaşam varolduğu andan bu yana, bitkisinden hayvanına kadar tümüyle
iletişim içinde olan mükemmel bir mekanizmayla donatılmış sistemlerle
sürmektedir. İnsan da, dünyaya getirildiği ilk andan, öldüğü ve toprağa geri
döndüğü son anına kadar fi ziki varlığını duyuları vasıtasıyla sürdürmektedir.
İnsanın duyuları onun dünya ile bağlantısını sağlayan temel iletişim
mekanizmasını oluşturmaktadır. İletişim süreci, tarihi ve teknolojik değişimler
neticesinde büyük dönüşümler yaşamıştır. Özellikle sanayi devrimi
ve sonrası için gerçekleşen teknolojik ilerlemeler bu değişimin ilk adımları
olmuştur. Sonrasında XXI. yüzyılla gelen büyük kırılma bilginin ve bilgiye
erişimin biçimini, amacını ve kullanım şeklini tümüyle farklılaştırmıştır.
İletişimde, insanın duyuları ile sağladığı fi ziki yapı büsbütün değişmiş, bununla
birlikte kolaylık ve hız adına faydacı bir yaklaşım esaslı teknolojik
ilerlemeler karşımıza çıkmıştır. İnsan, iletişim sürecine, aracı veya onu tamamen
devre dışı bırakacak bir ikame unsurlar ile sağlamaya başlayınca,
kontrolünün ve dünyaya bakışının da evrildiğini anlamaya başlamalıdır. Bu
yönüyle iletişim salt mesaj alıp vermeden öteye geçeli uzun yıllar olmuştur.
Artık, amlam, değer ve düşünce üreten mekanizmalara sahip bir algı dünyasında
simülasyon evreni içerisinde ikame bir kimlikle yaşayan bireyden
bahsetmemiz gerekmektedir. Bu noktada, medya internet ve yapay zeka
uygulamaları ve bu teknolojileri kulanan uygulamalar yeni medya olarak tanımlanmaktadır.
Yeni medyanın bireyle olan ilişki alanı en güçlü biçimde ve
sürekli biçimde enformasyonu sağladığı alan “sosyal medya” alanıdır. Vazgeçilmez
bir alışkanlık haline gelen sosyal medya, çoklu kullanıma elverişliliği,
sürekli güncellenebilmesi, sanal paylaşıma imkân tanıması noktalarıyla
bireye yönelik etkiliyici uygun bir ortam olarak, günümüzün dikkat çekiçi
mecralarının başında gelmektedir. Sosyal medya uygulamaları, bireylere çok
sayıda yeni ilgi çekici alan ve içerik sağlamaktadır.
... Ardından iki doğal dilin birbirine çevrildiği ilk bilgisayar programları gelmiştir. Yapılan ilk çeviri de Rusçadan İngilizceye yapılan 250 kelimelik bir çeviri olmuştur (Hutchins, 2004). Bu ilk denemeden sonra MÇ konusundaki çalışmalar inişli çıkışlı çeşitli dönemler yaşasa da günümüze kadar gelişerek gelmiş ve bugün ücretsiz servisler olarak elimizdeki cep telefonlarında bulunacak kadar yaygınlaşmışlardır. ...
... Bilgisayar bilimi alanında çalışan araştırmacılar insan dillerini modelleyerek programlama dillerini yaratmışlardır. Bundan önce ise dilin matematiksel yapısı modellenerek ilk bilgisayarların icadından bu yana MÇ bilgisayarların temel amaçlarından biri olmuştur (Hutchins, 2004). Bilgisayarlar ve çeviri konusu söz konusu olduğunda birbirine benzeyen ve kimi zaman birbirinin yerine kullanılan terimler mevcuttur. ...
... Deneyde 250 kelimelik bir Rusça metin İngilizceye tercüme edilmiştir Soğuk Savaş Dönemi'nin bu ilk yıllarında MÇ çalışmalarının Sovyetler Birliğinin resmi dili olan Rusçaya odaklanması anlaşılabilirdir. İlginç olan nokta deneyden sorumlu olan ve daha önce hem Başkan Roosvelt'in çevirmenliğini hem de BM ve Nünberg Mahkemelerindeki çeviri faaliyetlerinin organizasyonundan sorumlu olan Leon Dostert'in "birkaç yıl içerisinde MÇ'nin anlam belirsizliği problemini de aşarak makul çeviriler yapmaya elverişli hale geleceğini" duyurmasıdır (Hutchins, 2004). Ancak bu seviyeye ulaşılıp ulaşılamayacağı konusunda hala tartışmalar devam etmektedir. ...
Kitap içeriği, yeni medya çalışmalarının tarihsel gelişim süreçleri kapsamında karşılaştırmalı örnekler üzerinden oluşturulmuştur. Kitap; “kimlik ve tüketim”, “iletişim teknolojileri ve örnek kavramlar”, “reklam ve kullanım pratikleri” olmak üzere üç ana başlık altında temellendirilmiştir. Buradaki akademik yaklaşım, eleştirel bakış açısıyla hazırlanmıştır. Türkiye’nin farklı üniversitelerinden, alanlarında uzman akademisyenlerin katılımlarıyla özellikle kimlik, reklam ve tüketim konularında iddiası olan ve alanında yeni önermelerde bulunan bu çalışmaların literatüre katkı sağlayacağı umut edilmektedir.
Dr. Murat Birol-Dr. Yasin Söğüt
... From the historical perspective, the fields of artificial intelligence and education have been closely linked since the inception of AI, with early AI pioneers contributing to both areas by using AI to understand and improve human learning, a perspective that has since been argued to be diminished but still revivable [24]. Also, computational methods for performing transformations between different data modalities have existed, for example, for text-to-text, text-tospeech, and speech-to-text since the 1950s [22,38,44] and text to image since the 2000s [2]. Recently, deep learning has allowed the development of other transformations like text-to-video, video-totext [67], and text-to-music [10]. ...
... lack of precise conventions for naming used technologies, Ope-nAI ChatGPT seems to dominate the research concerning LLMs in education, which is not surprising considering the simplicity of its operation [e.g., 74] and prior similar review results [e.g., 5]. Including specific product names in the literature, search string can yield different results, but this research addressed literature that explicitly mentioned different generative models instead of specific software services.Text-to-speech was the next common keyword after language models followed by text-to-image, text-to-text, and speech-to-text, reflecting the maturity of technologies [i.e.,2,22,38,44] and diffusion of innovations into educational domain(Table 1, ...
Generative artificial intelligence (GenAI) can reshape education and learning. While large language models (LLMs) like ChatGPT dominate current educational research, multimodal capabilities—such as text-to-speech and text-to-image—are less explored. This study uses topic modeling to map the research landscape of multimodal and generative AI in education. An extensive literature search yielded 4175 articles. Employing a topic modeling approach, latent topics were extracted, resulting in 38 interpretable topics organized into 14 thematic areas. Findings indicate a predominant focus on text-to-text models in educational contexts, with other modalities underexplored, overlooking the broader potential of multimodal approaches. The results suggest a research gap, stressing the importance of more balanced attention across different AI modalities and educational levels. In summary, this research provides an overview of current trends in generative AI for education, underlining opportunities for future exploration of multimodal technologies to fully realize the transformative potential of artificial intelligence in education.
... 4 Multilingual Artificial Intelligence identified as the first substantial attempt for computers to solve non-numerical problems (Hutchins, 2004). Language is a natural area for computational and mathematical tools to demonstrate its power in synthesizing technological advantages and social influence. ...
... His idea was soon implemented, when the IBM-Georgetown experiment demonstrated a Russian-English MT system in New York in 1954. It was the first computer-based application related to multilingual natural language (Hutchins, 2004). Since then, multilingual AI has undergone multiple major breakthroughs, from modularized models, to end-to-end artificial neural networks (ANNs), progressing to generative large language models (LLMs). ...
... I would like to wrap up my thesis with an historical example. In January 1954, a major event occurred in New York City for all machine translation researchers: IBM and George Washington University presented a new machine translation system [97]. This system could automatically translate short sentences from Russian into English, which was an incredible success at the time. ...
... The expectations of contemporaries were very high: one of researchers, Léon Dostert, stated that "five, perhaps three, years hence, interlingual meaning conversion by electronic process in important functional areas of several languages may well be an accomplished fact." [97]. Two years later, the famous Dartmouth Summer Research Project on Artificial Intelligence took place, which is considered to be the founding event of the field of artificial intelligence [157]. ...
Phylogenetic placement determines possible phylogenetic origins of unknown query DNA or protein sequences, given a fixed reference phylogeny.Its main application is species identification, an essential bioinformatics problem with environmental ecology applications, microbial diversity studies, and medicine. Alignment-free methods for phylogenetic placement are a novel group of methods designed to eliminate the need to align query sequences within reference sequences --- a current limit to the applicability of phylogenetic placement methods in the next-generation sequencing (NGS) era.One of such methods is RAPPAS. It introduced the concept of phylogenetically aware k-mers (phylo-k-mers): k-mers paired with relevant probabilistic information about the reference phylogeny. This information determines how probable it is to observe any k-mer in hypothetical sequences arising from different parts of the reference tree. RAPPAS preprocesses the reference phylogenetic tree and alignment, computing phylo-k-mers. This allows fast phylogenetic placement of vast amounts of query sequences; however, the computation of phylo-k-mers is expensive in both running time and memory.This thesis studies the problem of effective indexing of reference phylogenies with phylo-k-mers. Chapter 1 gently introduces the reader to the problem. Starting with a historical overview of biology and bioinformatics of the last decades, it discusses the importance of sequence identification in modern bioinformatics, overwhelmed with amounts of sequencing data produced by NGS technologies. Then, it overviews existing methods of phylogenetic placement and discusses their limitations.Chapter 2 describes and analyzes the existing solution for the central algorithmic problem of phylo-k-mer computation: computing phylo-k-mers for one node in a k-sized window of the reference alignment. In addition, it describes a novel algorithm for this problem based on the divide-and-conquer approach. This algorithm improves the existing solution both theoretically and in practice.Chapter 3 proposes a novel method of filtering phylo-k-mers based on Mutual Information. This method allows reducing memory consumption of phylogenetic placement significantly with a negligible decrease in placement accuracy. It also describes how RAPPAS is connected to well-studied methods of text classification with Naive Bayes.Finally, Chapter 4 presents two new phylo-k-mer-related tools: XPAS for efficient computation of phylo-k-mers and RAPPAS2, an effective reimplementation of RAPPAS. Experimental results provided show that XPAS and RAPPAS2 outperform RAPPAS both in running speed and memory consumption. Both tools are written in modern C++, optimized for efficiency, and are ready to use.The final chapter discusses possible directions of future work on phylo-k-mer-related methods, the challenges that are yet to be overcome, and a discussion on the future of phylogenetic placement.
... NLP wordt ook gebruikt om teksten te vertalen. De eerste automatische vertaling vond plaats bij het Georgetown-IBM-experiment(Hutchins, 2004), waarin 250 woorden via zes grammaticale regels werden vertaald van het Russisch naar het Engels. Hierna bleef de vooruitgang lange tijd minimaal. ...
De impact die lectoren, hoogleraren en andere onderzoekers hebben op de maatschappij,
kan groot zijn. Voor de problemen van nu kunnen zij gezamenlijk antwoorden
vinden die (hopelijk) zullen leiden tot een oplossing. Maar welk probleem er morgen op
hun pad komt, is niet te voorspellen. En wat er morgen allemaal mogelijk is om
problemen aan te pakken, ook niet. Onderzoekers herkennen en erkennen dat ook zij
een beperkte houdbaarheid hebben.
Onderzoek moet niet alleen leiden tot oplossingen voor nu, maar ook de studenten,
dus de professionals van de toekomst, meenemen in hoe zij straks die nieuwe
problemen kunnen benaderen en met welke middelen ze dat kunnen doen. Dat
betekent dat zij een intrinsiek onderdeel zouden moeten zijn van al het onderzoek en
alle innovatie. Dit is waar wij als lectoraat AI & Data Supported Healthcare voor staan:
een plek zijn waar iedereen kan bijdragen aan de oplossingen van nu, maar ook kan
leren voor de problemen van morgen. Waar wetenschap, zorg, technologische
innovatie en onderwijs hand in hand gaan. Waar alle ideeën die kunnen leiden tot
een oplossing een plek kunnen krijgen, waar geëxperimenteerd kan worden, waar je
mag falen en daarvan mag leren en waar we samen echt verder komen.
Het lectoraat is ontstaan vanuit een gemeenschap van onderzoekers, studenten en
docenten die uiteindelijk samen een beweging hebben gevormd die binnen
Hogeschool Rotterdam de ontwikkelingen heeft aangejaagd maar ook verduurzaamd.
Dat we van origine uit de praktijk komen, is ook onze grootste kracht. Dat vormt
de basis van waaruit wij verder zullen groeien en ook anderen verder zullen laten
groeien, zodat zij hun eigen weg kunnen vinden, nu en in de toekomst.
... The central idea was to use bilingual dictionaries for word-level mappings and predefined rules to handle grammatical transformations. Early RBMT systems, such as the Georgetown-IBM experiment (1954) [18], demonstrated the feasibility of MT by translating a set of Russian sentences into English, albeit under highly constrained conditions. RBMT methods followed one of three primary strategies: direct [17], transfer-based [19], or interlingua-based translation [20]. ...
Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, these models face challenges when applied to low-resource languages due to limited training data and difficulty in understanding cultural nuances. In this paper, we propose QueEn, a novel approach for Quechua-English translation that combines Retrieval-Augmented Generation (RAG) with parameter-efficient fine-tuning techniques. Our method leverages external linguistic resources through RAG and uses Low-Rank Adaptation (LoRA) for efficient model adaptation. Experimental results show that our approach substantially exceeds baseline models, with a BLEU score of 17.6 compared to 1.5 for standard GPT models. The integration of RAG with fine-tuning allows our system to address the challenges of low-resource language translation while maintaining computational efficiency. This work contributes to the broader goal of preserving endangered languages through advanced language technologies.
... The evolution of NLP has transformed computers from simple data processors into intelligent systems capable of interpreting, manipulating, and understanding everyday human language [2]. The earliest significant from of NLP was in machine translation, where text was directly translated from Russian to English on a word-for-word basis [3]. The "mechanical translator" knew only 250 words of Russian and 6 grammar rules to combine them. ...
Despite significant advances in quantum computing across various domains, research on applying quantum approaches to language compositionality - such as modeling linguistic structures and interactions - remains limited. This gap extends to the integration of quantum language data with real-world data from sources like images, video, and audio. This thesis explores how quantum computational methods can enhance the compositional modeling of language through multimodal data integration. Specifically, it advances Multimodal Quantum Natural Language Processing (MQNLP) by applying the Lambeq toolkit to conduct a comparative analysis of four compositional models and evaluate their influence on image-text classification tasks. Results indicate that syntax-based models, particularly DisCoCat and TreeReader, excel in effectively capturing grammatical structures, while bag-of-words and sequential models struggle due to limited syntactic awareness. These findings underscore the potential of quantum methods to enhance language modeling and drive breakthroughs as quantum technology evolves.
... For NLP, the latest development in word embedding has made advanced strides [14]- [17]. Firstly, a statistical NLP replaced the symbolic NLP to overcome the scalability issue [18], [19]. These NLP models use statistical inference to learn the rules automatically [20]. ...
The Tsetlin Machine (TM) has gained significant attention in Machine Learning (ML). By employing logical fundamentals, it facilitates pattern learning and representation, offering an alternative approach for developing comprehensible Artificial Intelligence (AI) with a specific focus on pattern classification in the form of conjunctive clauses. In the domain of Natural Language Processing (NLP), TM is utilised to construct word embedding and describe target words using clauses. To enhance the descriptive capacity of these clauses, we study the concept of Reasoning by Elimination (RbE) in clauses' formulation, which involves incorporating feature negations to provide a more comprehensive representation. In more detail, this paper employs the Tsetlin Machine Auto-Encoder (TM-AE) architecture to generate dense word vectors, aiming at capturing contextual information by extracting feature-dense vectors for a given vocabulary. Thereafter, the principle of RbE is explored to improve descriptivity and optimise the performance of the TM. Specifically, the specificity parameter s and the voting margin parameter T are leveraged to regulate feature distribution in the state space, resulting in a dense representation of information for each clause. In addition, we investigate the state spaces of TM-AE, especially for the forgotten/excluded features. Empirical investigations on artificially generated data, the IMDB dataset, and the 20 Newsgroups dataset showcase the robustness of the TM, with accuracy reaching 90.62\% for the IMDB.
... Natural Language Processing (NLP) has evolved significantly, starting from its inception in the 1950s with early endeavors like the Georgetown-IBM experiment, which laid the groundwork for machine translation [5]. Subsequent decades saw advancements in chatbots such as ELIZA and PARRY, incorporating statistical techniques like Hidden Markov Models in the late 1980s [6]. ...
In this work, we evaluated the efficacy of Google's Pathways Language Model (GooglePaLM) in analyzing sentiments expressed in product reviews. Although conventional Natural Language Processing (NLP) techniques such as the rule-based Valence Aware Dictionary for Sentiment Reasoning (VADER) and the long sequence Bidirectional Encoder Representations from Transformers (BERT) model are effective, they frequently encounter difficulties when dealing with intricate linguistic features like sarcasm and contextual nuances commonly found in customer feedback. We performed a sentiment analysis on Amazon's fashion review datasets using the VADER, BERT, and GooglePaLM models, respectively, and compared the results based on evaluation metrics such as precision, recall, accuracy correct positive prediction, and correct negative prediction. We used the default values of the VADER and BERT models and slightly finetuned GooglePaLM with a Temperature of 0.0 and an N-value of 1. We observed that GooglePaLM performed better with correct positive and negative prediction values of 0.91 and 0.93, respectively, followed by BERT and VADER. We concluded that large language models surpass traditional rule-based systems for natural language processing tasks.
... NLP is a sub-branch of artificial intelligence that concerns a computer's ability to interpret, manipulate, and comprehend human language. The field of NLP has its roots in the Georgetown-IBM experiment from the 1950s, where researchers could automatically translate Russian to English [27]. NLP has since significantly improved and grown into areas such as text prediction for auto-correction, writing assistance, translation between languages, and chatbots. ...
Recent advances in natural language processing have increased interest in automatic question generation, particularly in education (e.g., math, biology, law, medicine, and languages) due to its efficiency in assessing comprehension. Specifically, multiple-choice questions have become popular, especially in standardized language proficiency tests. However, manually creating high-quality tests is time-consuming and challenging. Distractor generation, a critical aspect of multiple-choice question creation, is often overlooked, yet it plays a crucial role in test quality. Generating appropriate distractors requires ensuring they are incorrect but related to the correct answer (semantically or contextually), are grammatically correct, and of similar length to the target word. While various languages have seen research in automatic distractor generation, Japanese has received limited attention. This paper addresses this gap by automatically generating cloze tests, including distractors, for Japanese language proficiency tests, evaluating the generated questions’ quality, difficulty, and preferred distractor types, and comparing them to human-made questions through automatic and manual evaluations.
... Machine translation (MT) refers to input text in one language translated into output text of another language without human intervention. Stemming from a 1954 small-scale military experiment (Hutchins, 2004), today's MT use is widespread "because of its convenience, multilingualism, immediacy, efficiency, and free cost" (Lee, 2020, p.1-2). MT has cycled through different models. ...
The COVID-19 pandemic has elevated focus on educational technologies (Elaish, et al., 2021). One area of sustained controversy in this domain centers around machine translation (MT), where language teachers and students have historically disagreed (Lee, 2020). While research has demonstrated the benefits of MT (e.g. Benda, 2013; Chon et al. 2021; Correa, 2014; Dziemianko, 2017; Enkin & Mejías-Bikandi, 2016; Garcia & Pena, 2011; Lee, 2020; Lee & Briggs, 2021) and studies have consistently reported frequent student usage of MT (e.g. Alhaisoni & Alhaysony, 2017) Clifford, Merschel, & Munné, 2013; Jin & Diefell, 2013; Tsai, 2019; Yang & Wang, 2019), teacher views have traditionally been negative (e.g. Case; 2015; Clifford, Merschel, & Munné, 2013; Niño, 2009; Stapleton & Leung Ka Kin, 2019). Given that recent research on MT has targeted ESOL (e.g. Lee, 2020; Murphy Odo, 2019; Tsai, 2019), that MT itself has evolved considerably since 2016 (Yang & Wang, 2019), and that teacher beliefs can be influenced by professional development and context (Borg, 2015), this study examined (1) contemporary attitudes toward and practices around MT among students (n=75) and teachers (n=25) of diverse languages, and (2) changes in instructor views after high impact pedagogical events: (a) a professional development seminar specifically on MT and (b) the “crisis‐prompted [shift to] remote language teaching” (Gacs et al, 2020) as a result of the global COVID-19 pandemic. Results from four surveys indicate a wide, enduring chasm between students, who increasingly use and feel positively towards MT but are varied in their understanding of implications of its use for academic integrity, and teachers, most of whom make no instructional use of MT, feel negatively about it, have clearer reviews on its relationship to academic integrity, and maintain their views after specific professional development and broad and far-reaching contextual events related to technology. Implications for practice, especially in the context of a surge in academic integrity violations related to MT during the COVID-19 pandemic (Çelik & Lancaster, 2021), will be discussed.
... In January 1954, a joint project conducted by IBM and Georgetown University showcased a Russian-English MT system in New York. This was the frst computer-based application related to natural language (Hutchins, 2004). ...
... Introduction later, NLP goes from philosophical speculation to experimental demonstration: the IBM 701 computer successfully translated sentences from Russian to English such as "They produce alcohol out of potatoes." [Hut04]. With only six grammatical rules and a 250-word vocabulary taken from organic chemistry and other general topics, this first experiment generated a great deal of public attention and the overly-optimistic prediction that machine translation would be an accomplished task in "five, perhaps three" years. ...
This thesis introduces quantum natural language processing (QNLP) models based on a simple yet powerful analogy between computational linguistics and quantum mechanics: grammar as entanglement. The grammatical structure of text and sentences connects the meaning of words in the same way that entanglement structure connects the states of quantum systems. Category theory allows to make this language-to-qubit analogy formal: it is a monoidal functor from grammar to vector spaces. We turn this abstract analogy into a concrete algorithm that translates the grammatical structure onto the architecture of parameterised quantum circuits. We then use a hybrid classical-quantum algorithm to train the model so that evaluating the circuits computes the meaning of sentences in data-driven tasks. The implementation of QNLP models motivated the development of DisCoPy (Distributional Compositional Python), the toolkit for applied category theory of which the first chapter gives a comprehensive overview. String diagrams are the core data structure of DisCoPy, they allow to reason about computation at a high level of abstraction. We show how they can encode both grammatical structures and quantum circuits, but also logical formulae, neural networks or arbitrary Python code. Monoidal functors allow to translate these abstract diagrams into concrete computation, interfacing with optimised task-specific libraries. The second chapter uses DisCopy to implement QNLP models as parameterised functors from grammar to quantum circuits. It gives a first proof-of-concept for the more general concept of functorial learning: generalising machine learning from functions to functors by learning from diagram-like data. In order to learn optimal functor parameters via gradient descent, we introduce the notion of diagrammatic differentiation: a graphical calculus for computing the gradients of parameterised diagrams.
... D'autre part, les discours qu'elle génère se distinguent quant à eux par la polarisation des débats et par les exagérations notables de part et d'autre de ces discussions [Loock, 2020, Cambreleng, 2020. Les titres accrocheurs relevés dans la presse ne sont évidemment pas chose nouvelle et se retrouvent parfois presque mot pour mot dans des coupures de journaux dont Hutchins [2004] notait déjà le caractère spéculatif et optimiste à la suite de l'expérience de Georgetown en 1954. Il s'agit là, en effet, d'une tradition au moins aussi vieille que le Turc mécanique du XVIII e siècle. ...
La traduction automatique neuronale et son adaptation à des domaines spécifiques par le biais de corpus spécialisés ont permis à cette technologie d’intégrer bien plus largement qu’auparavant le métier et la formation des traducteur·trice·s. Si le paradigme neuronal (et le deep learning de manière générale) a ainsi pu investir des domaines parfois insoupçonnés, y compris certains où la créativité est de mise, celui-ci est moins marqué par un gain phénoménal de performance que par une utilisation massive auprès du public et les débats qu’il génère, nombre d’entre eux invoquant couramment le cas littéraire pour (in)valider telle ou telle observation. Pour apprécier la pertinence de cette technologie, et ce faisant surmonter les discours souvent passionnés des opposants et partisans de la traduction automatique, il est toutefois nécessaire de mettre l’outil à l’épreuve, afin de fournir un exemple concret de ce que pourrait produire un système entraîné spécifiquement pour la traduction d’œuvres littéraires. Inscrit dans un projet de recherche plus vaste visant à évaluer l’aide que peuvent fournir les outils informatiques aux traducteurs et traductrices littéraires, cet article propose par conséquent une expérience de traduction automatique de la prose qui n’a plus été tentée pour le français depuis les systèmes probabilistes et qui rejoint un nombre croissant d’études sur le sujet pour d’autres paires de langues. Nous verrons que si les résultats sont encourageants, ceux-ci laissent présager une tout autre manière d’envisager la traduction automatique, plus proche de la traduction humaine assistée par ordinateur que de la post-édition pure, et que l’exemple des œuvres de littérature soulève en outre des réflexions utiles pour la traduction dans son ensemble.
... Early approaches sought to expand the relatively rule-friendly field of cryptography to natural languages [61]. The 1954 Georgetown-IBM experiment demonstrated Russian-English translation on an IBM 701, based on only six rules [27]. This model was based on several fundamental logical rules, and its output was limited to a carefully selected corpus. ...
Ludwig Wittgenstein's theory of language games introduced a philosophically unorthodox understanding to the meaning and the use of language. Modern natural language processing (NLP) approaches focus on context-derived models of meaning, avoidance of syntactically defined rules, and rely on large bodies of data to statistically approximate our real-world context. This paper traces the origin, development, and intersection of Wittgenstein intellectual legacy and its relevancy to NLP, assesses the ways his thoughts have influenced it, and examines how his work could be better applied in future directions of NLP.
... The first examples of a rudimentary machine translation from Russian to English came at the IBM Artificial Intelligence in Language Learning 5 conference in Georgetown in 1954. During the demonstration, more than 60 sentences were converted from Russian to English, thanks to the IBM 701 (Hutchins, 2004). First, all words in an English vocabulary are indexed on tape and assigned numerical codes. ...
The role of Artificial Intelligence (AI) is rapidly growing because of the use of different technologies adapted to language learning: be it in a physical classroom or distant learning, especially after the impact of COVID-19, and it will further grow in the upcoming years. A report on the global forecast for 2027 says, “the online language learning market is expected to grow at a CAGR of 18.7% from 2020 to 2027 to reach $21.2 billion by 2027” (Meticulous Research, 2020). It is not difficult to predict that Artificial Intelligence-based apps with graphical User Interfaces (UI) will eventually replace books. This paper highlights the practical underpinning of using Artificial Intelligence (AI) in Language Learning (LL) and how it will gradually shift the focus of traditional language learning to Artificial Intelligence (AI) in 21st-century learning and instructional contexts. The implication of this paper is towards a better understanding of using different Artificial Intelligence (AI) tools. The paper concludes on a note of how stable Artificial Intelligence (AI) is, what are the trending technologies in Artificial Intelligence (AI), and what kind of changes it brings to language learning and Natural Language Processing (NLP) based on complex algorithms and mathematical calculations.
Keywords: Artificial Intelligence (AI), Language Learning (LL), Natural Language Processing (NLP), Machine Learning, Deep Learning, Human-Robot Interaction (HRI)
... Since very early attempts, see the Georgetown experiment in 1954 (Hutchins, 2004), and later followed by the statistical approaches (Berger et al., 1994), machine translation has been formalized as the task of translating a single sentence in the source language to a single sentence in the target language. This simplification generally persists even today; it is built deeply into the interfaces of machine translation services and computer-assisted tools. ...
Our book "The Reality of Multi-Lingual Machine Translation" discusses the benefits and perils of using more than two languages in machine translation systems. While focused on the particular task of sequence-to-sequence processing and multi-task learning, the book targets somewhat beyond the area of natural language processing. Machine translation is for us a prime example of deep learning applications where human skills and learning capabilities are taken as a benchmark that many try to match and surpass. We document that some of the gains observed in multi-lingual translation may result from simpler effects than the assumed cross-lingual transfer of knowledge. In the first, rather general part, the book will lead you through the motivation for multi-linguality, the versatility of deep neural networks especially in sequence-to-sequence tasks to complications of this learning. We conclude the general part with warnings against too optimistic and unjustified explanations of the gains that neural networks demonstrate. In the second part, we fully delve into multi-lingual models, with a particularly careful examination of transfer learning as one of the more straightforward approaches utilizing additional languages. The recent multi-lingual techniques, including massive models, are surveyed and practical aspects of deploying systems for many languages are discussed. The conclusion highlights the open problem of machine understanding and reminds of two ethical aspects of building large-scale models: the inclusivity of research and its ecological trace.
... Machine translation (MT) refers to input text in one language translated into output text of another language without human intervention. Stemming from a 1954 small-scale military experiment (Hutchins, 2004), today's MT use is widespread "because of its convenience, multilingualism, immediacy, efficiency, and free cost" (Lee, 2020, p.1-2). MT has cycled through different models. ...
The COVID-19 pandemic has elevated focus on educational technologies (Elaish, et al., 2021). One area of sustained controversy in this domain centers around machine translation (MT), where language teachers and students have historically disagreed (Lee, 2020). While research has demonstrated the benefits of MT (e.g. Benda, 2013; Chon et al. 2021; Correa, 2014; Dziemianko, 2017; Enkin & Mejías-Bikandi, 2016; Garcia & Pena, 2011; Lee, 2020; Lee & Briggs, 2021) and studies have consistently reported frequent student usage of MT (e.g. Alhaisoni & Alhaysony, 2017) Clifford, Merschel, & Munné, 2013; Jin & Diefell, 2013; Tsai, 2019; Yang & Wang, 2019), teacher views have traditionally been negative (e.g. Case; 2015; Clifford, Merschel, & Munné, 2013; Niño, 2009; Stapleton & Leung Ka Kin, 2019). Given that recent research on MT has targeted ESOL (e.g. Lee, 2020; Murphy Odo, 2019; Tsai, 2019), that MT itself has evolved considerably since 2016 (Yang & Wang, 2019), and that teacher beliefs can be influenced by professional development and context (Borg, 2015), this study examined (1) contemporary attitudes toward and practices around MT among students (n=75) and teachers (n=25) of diverse languages, and (2) changes in instructor views after high impact pedagogical events: (a) a professional development seminar specifically on MT and (b) the “crisis‐prompted [shift to] remote language teaching” (Gacs et al, 2020) as a result of the global COVID-19 pandemic. Results from four surveys indicate a wide, enduring chasm between students, who increasingly use and feel positively towards MT but are varied in their understanding of implications of its use for academic integrity, and teachers, most of whom make no instructional use of MT, feel negatively about it, have clearer reviews on its relationship to academic integrity, and maintain their views after specific professional development and broad and far-reaching contextual events related to technology. Implications for practice, especially in the context of a surge in academic integrity violations related to MT during the COVID-19 pandemic (Çelik & Lancaster, 2021), will be discussed.
... For example, natural language inference (NLI) can be considered to be an enabling task, for catalyzing progress in the ability to solve logical reasoning problems. For the current discussion, we consider the particular case of MT, which is one of the oldest and most researched fields in AI (Hutchins, 2004), as well as one of the most deployed applications of NLP. MT is a sequenceto-sequence task with the objective of translating a sentence from a source to a target language. ...
Recent advances in AI and ML applications have benefited from rapid progress in NLP research. Leaderboards have emerged as a popular mechanism to track and accelerate progress in NLP through competitive model development. While this has increased interest and participation, the over-reliance on single, and accuracy-based metrics have shifted focus from other important metrics that might be equally pertinent to consider in real-world contexts. In this paper, we offer a preliminary discussion of the risks associated with focusing exclusively on accuracy metrics and draw on recent discussions to highlight prescriptive suggestions on how to develop more practical and effective leaderboards that can better reflect the real-world utility of models.
... On peut retracer les origines des premières études sur la TA au début des années 1950, avec notamment l'organisation de la première conférence dédiée à cette tâche en 1952, et l'expérience de Georgetown-IBM en 1954, une démonstration publique d'un système de traduction russe-anglais pouvant gérer un vocabulaire de 250 mots et six règles grammaticales (Hutchins, 1986(Hutchins, , 2004. ...
La désambiguïsation lexicale (DL) et la traduction automatique (TA) sont deux tâches centrales parmi les plus anciennes du traitement automatique des langues (TAL). Bien qu'ayant une origine commune, la DL ayant été conçue initialement comme un problème fondamental à résoudre pour la TA, les deux tâches ont par la suite évolué très indépendamment. En effet, d'un côté, la TA a su s'affranchir d'une désambiguïsation explicite des termes grâce à des modèles statistiques et neuronaux entraînés sur de grandes quantités de corpus parallèles, et de l'autre, la DL, qui est confrontée à certaines limitations comme le manque de ressources unifiées et un champs d'application encore restreint, reste un défi majeur pour permettre une meilleure compréhension de la langue en général.Aujourd'hui, dans un contexte où les méthodes à base de réseaux de neurones et les représentations vectorielles des mots prennent de plus en plus d'ampleur dans la recherche en TAL, les nouvelles architectures neuronales et les nouveaux modèles de langue pré-entraînés offrent non seulement de nouvelles possibilités pour développer des systèmes de DL et de TA plus performants, mais aussi une opportunité de réunir les deux tâches à travers des modèles neuronaux joints, permettant de faciliter l'étude de leurs interactions.Dans cette thèse, nos contributions porteront dans un premier temps sur l'amélioration des systèmes de DL, par l'unification des données nécessaires à leur mise en oeuvre, la conception de nouvelles architectures neuronales et le développement d'approches originales pour l'amélioration de la couverture et des performances de ces systèmes. Ensuite, nous développerons et comparerons différentes approches pour l'intégration de nos systèmes de DL état de l'art et des modèles de langue, dans des systèmes de TA, pour l'amélioration générale de leur performance. Enfin, nous présenterons une nouvelle architecture pour l'apprentissage d'un modèle neuronal joint pour la DL et la TA, s'appuyant sur nos meilleurs systèmes neuronaux pour l'une et l'autre tâche.
... In 1954, a first program written at Georgetown University made it possible to translate several dozen simple sentences. The program uses 250 words and only 6 grammar rules, and runs on an IBM 701(Hutchins, 2004). Von Neumann's work on the architecture of a calculator and Turing's work on the theorization of calculable functions by machines(Benko & Lányi, 2009;Godfrey & Hendry, 1993;Haenlein & Kaplan, 2019;McCorduck et al., 1977). ...
Purpose: The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (i) analysis of AI and AI concepts/technologies; (ii) in-depth exploration of case studies from a great number of industrial sectors; (iii) data collection from the databases (websites) of AI-based solution providers; and (iv) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations.
Design/methodology/approach: This study has called on the theory on IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase.
Findings: AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can therefore enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes.
Implications: AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared towards a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations and, at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships, and scalable infrastructure.
Originality/Value: This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 cases studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.
The application of natural language processing (NLP) technology in the field of education has attracted considerable attention. This study takes 716 articles from the Web of Science database from 1998 to 2023 as its research sample. Using bibliometrics as the theoretical foundation, and employing methods such as literature review and knowledge mapping analysis, the study utilizes tools like CiteSpace to generate relevant visualizations, analyzing key research themes, frontier developments, and providing future prospects in this domain. The main findings of the study are as follows: First, the number of publications in this field has been increasing annually, forming core publishing journals such as Education and Information Technology, core research teams led by figures like Cucchiarini Catia and Meurers Detmar, and core publishing countries including the United States and China. Second, the field primarily covers five major themes: the educational application of technical tools, the analysis and development of educational content, the application of computational linguistics in education, language acquisition and language learning, and educational assessment and analysis methods. Third, the research in this field exhibits certain developmental phases, progressing through the stages of emergence, exploration, and development. Based on these findings, the following future prospects are proposed: at the theoretical level, deeper application of personalized learning paths, emotional monitoring and learning support, and intelligent generation and optimization of educational content; at the practical level, interdisciplinary collaboration and innovation, educational data mining and analysis, and global perspectives with international cooperation.
Curated resources at centralized repositories provide high-value service to users by enhancing data veracity. Curation, however, comes with a cost, as it requires dedicated time and effort from personnel with deep domain knowledge. In this paper, we investigate the performance of a large language model (LLM), specifically generative pre-trained transformer (GPT)-3.5 and GPT-4, in extracting and presenting data against a human curator. In order to accomplish this task, we used a small set of journal articles on wheat and barley genetics, focusing on traits, such as salinity tolerance and disease resistance, which are becoming more important. The 36 papers were then curated by a professional curator for the GrainGenes database (https://wheat.pw.usda.gov). In parallel, we developed a GPT-based retrieval-augmented generation question-answering system and compared how GPT performed in answering questions about traits and quantitative trait loci (QTLs). Our findings show that on average GPT-4 correctly categorized manuscripts 97% of the time, correctly extracted 80% of traits, and 61% of marker–trait associations. Furthermore, we assessed the ability of a GPT-based DataFrame agent to filter and summarize curated wheat genetics data, showing the potential of human and computational curators working side-by-side. In one case study, our findings show that GPT-4 was able to retrieve up to 91% of disease related, human-curated QTLs across the whole genome, and up to 96% across a specific genomic region through prompt engineering. Also, we observed that across most tasks, GPT-4 consistently outperformed GPT-3.5 while generating less hallucinations, suggesting that improvements in LLM models will make generative artificial intelligence a much more accurate companion for curators in extracting information from scientific literature. Despite their limitations, LLMs demonstrated a potential to extract and present information to curators and users of biological databases, as long as users are aware of potential inaccuracies and the possibility of incomplete information extraction.
Background
Depression is a prevalent global mental health disorder with substantial individual and societal impact. Natural language processing (NLP), a branch of artificial intelligence, offers the potential for improving depression screening by extracting meaningful information from textual data, but there are challenges and ethical considerations.
Objective
This literature review aims to explore existing NLP methods for detecting depression, discuss successes and limitations, address ethical concerns, and highlight potential biases.
Methods
A literature search was conducted using Semantic Scholar, PubMed, and Google Scholar to identify studies on depression screening using NLP. Keywords included “depression screening,” “depression detection,” and “natural language processing.” Studies were included if they discussed the application of NLP techniques for depression screening or detection. Studies were screened and selected for relevance, with data extracted and synthesized to identify common themes and gaps in the literature.
Results
NLP techniques, including sentiment analysis, linguistic markers, and deep learning models, offer practical tools for depression screening. Supervised and unsupervised machine learning models and large language models like transformers have demonstrated high accuracy in a variety of application domains. However, ethical concerns related to privacy, bias, interpretability, and lack of regulations to protect individuals arise. Furthermore, cultural and multilingual perspectives highlight the need for culturally sensitive models.
Conclusions
NLP presents opportunities to enhance depression detection, but considerable challenges persist. Ethical concerns must be addressed, governance guidance is needed to mitigate risks, and cross-cultural perspectives must be integrated. Future directions include improving interpretability, personalization, and increased collaboration with domain experts, such as data scientists and machine learning engineers. NLP’s potential to enhance mental health care remains promising, depending on overcoming obstacles and continuing innovation.
Introdution: In recent years, the philosophy of language has undergone significant transformations thanks to developments in artificial intelligence and brain-machine interfaces (BMI). The introduction of the fourth version of Chat GPT and advancements in BMIs, such as Elon Musk's Neuralink, mark the beginning of a new era in human-machine communication. This study compares natural and artificial languages in light of recent debates on the use of artificial intelligence for the simulation of natural languages (Large Language Models, LLM). Metodologhia: The analysis reveals that Chat GPT technology is complementary to, rather than competitive with, human linguistic capabilities. LLMs excel in formal linguistic competence but are limited in the functional understanding of language, highlighting the distinction between language processing in the human brain and the functioning of LLMs. Furthermore, BMIs are opening new possibilities for direct communication between human brains and machines, as demonstrated by BrainNet projects. Results: These developments raise ethical and philosophical questions regarding the ownership of thoughts and individual integrity. Methodologically, the study examines neuroscientific research through philosophical analysis to highlight theoretical-conceptual implications. Conclusion: The conclusions suggest that while advanced technologies offer new communication perspectives, a thorough reflection on ethical and legal issues is necessary for responsible use.
تهدف هذه الدراسة إلى معرفة مدى معرفة المترجمين اليمنيين للتقنيات الحديثة الخاصة بالمترجمين ومدى استخدامهم لها. تم استخدام المنهج الوصفي التحليلي في البحث والاستبيان أداةً لجمع البيانات الأساسية اللازمة للبحث. ركزت الدراسة على الوضع الحالي للترجمة والمترجمين في اليمن وخاصة مدينتي تعز وإب كعينة لتطبيق الدراسة، حيث تم اخذ المترجمين المرخصين العاملين في مكاتب الترجمة كعينة للبحث. مع التطور المتصارع في التقنيات برزت في الآونة الأخيرة كثير من البرامج المتخصصة والمتعلقة بالترجمة مثل: Trados Workbench وwordfast وmemoQ وDéjàVuX وSDLX وStar Transit وMultiTrans وSimilis وMetaTexis وOmegat وغيرها، والتي يفترض على أي متخصص في الترجمة معرفتها واستخداماتها وكيفية الاستفادة منها في تسهيل عملية الترجمة. توصل البحث إلى العديد من النتائج، أهمها ضعف استخدام البرامج المساعدة في الترجمة، وقدم توصيات أهمها، إدراج مقرر الترجمة بمساعدة الحاسوب في المناهج الدراسية الجامعية في كل برامج البكالوريوس في اللغة الانجليزية والترجمة والقيام بتأهيل المترجمين في الخدمة وتدريبهم على استخدام التقنيات الحديثة وتطوير مهاراتهم اللازمة للقيام بالعمل بكل سهولة ويسر وبأقل التكاليف والوقت.
With the rapid growth of internet usage, a new situation has been created that enables practicing bullying. Cyberbullying has increased over the past decade, and it has the same adverse effects as face-to-face bullying, like anger, sadness, anxiety, and fear. With the anonymity people get on the internet, they tend to be more aggressive and express their emotions freely without considering the effects, which can be a reason for the increase in cyberbullying and it is the main motive behind the current study. This study presents a thorough background of cyberbullying and the techniques used to collect, preprocess, and analyze the datasets. Moreover, a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages, and it was deduced that there is significant room for improvement in the Arabic language. As a result, the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing (NLP) for the classification of Arabic datasets duly collected from Twitter (also known as X). In this regard, support vector machine (SVM), Naïve Bayes (NB), Random Forest (RF), Logistic regression (LR), Bootstrap aggregating (Bagging), Gradient Boosting (GBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) were shortlisted and investigated due to their effectiveness in the similar problems. Finally, the scheme was evaluated by well-known performance measures like accuracy, precision, Recall, and F1-score. Consequently, XGBoost exhibited the best performance with 89.95% accuracy, which is promising compared to the state-of-the-art.
In today’s world, online platforms such as social media, philanthropic communities, and Q &A websites provide opportunities for people to be altruistic by donating money or answering questions without expecting anything in return. The r/Random Acts Of Pizza subreddit on Reddit is one such online community where users can post requests for free pizza while explaining their current situation, and the outcome of each request is either successful or unsuccessful. This study seeks to explore the determinants that impact the outcome of such selfless appeals. To achieve this, we propose a new model architecture that combines two models, one that deals with sparse text vectors and the other that analyzes dense features from previous works to predict the success of a request. The study reveals that the probability estimated from the first model and the number of comments on the request post is crucial in predicting the outcome of a request.
Artificial intelligence technologies are currently challenging both machine translation and natural language processing. A better understanding of the social, economic and ethical stakes is urgently needed. This collective work explores the possibility and the contours of a new consensus between the human uses of language and the contributions of the machine.
One of the major challenges posed by machine translation (MT) when translating between English and Arabic is idiomatic expressions since their meanings are not taken literally but have a cultural meaning behind them, and they are “all in all metaphorical and cannot be understood directly” (Ghazala, 2003). Therefore, the main aim of the present study is to evaluate and compare the performance of three online MT systems namely, Google, Microsoft Bing, and Systran in translating English idiomatic expressions into Arabic in online newspapers. To achieve the goals of the present study, twenty idiomatic expressions which are the most frequently used in six British and American online newspapers have been identified, fed, discussed, compared and analyzed qualitatively by the researchers. Findings of the present study show that although the three online MT systems were not able to provide accurate translations of the English idiomatic expressions into Arabic in six online newspapers, Microsoft Bing machine is rated the best and has a better performance. Whereas Googles’s translation is found the least accurate as some words were not translated at all, and others were mistranslated. The study concludes that MT systems can be improved to get a better quality and provide an accurate translation if they deal with the idiomatic expressions as a unit and not individually. Since these are fixed idiomatic expressions, they are usually resolved using a simple memory translation, which sets side-by-side the English and the Arabic expressions without much cognitive effort.
Text-to-Speech Synthesis (TTS) is an active area of research to generate synthetic speech from underlying text. The identified syllables are uttered with proper duration and prosody characteristics to emulate natural speech. It falls under the category of Natural Language Processing (NLP), which aims to bridge the gap in communication between human and machine. So far as Western languages like English are concerned, the research to produce intelligent and natural synthetic speech has advanced considerably. But in a multilingual state like India, many regional languages viz. Malayalam is underexplored when it comes to NLP. In this article, we try to amalgamate the major research works performed in the area of TTS in English and the prominent Indian languages, with a special emphasis on the South Indian language, Malayalam. This review intends to provide right direction to the research activities in the language, in the area of TTS.
The rapid technological development has created new opportunities for language digitalization and the development of language technology applications. The core element of language technology is language resources, which is in a broad sense, can be considered as a scope of the databases that consists of the myriad of texts both in oral and written forms and used in the machine-learning algorithm. The creation of language resources requires two processes: the first one is language digitalization, meaning the transformation of the speech and texts into the machine-responsible form. The second process refers to text mining, which analyzes data by using a machine-learning algorithm. Adoption of the General Data Protection Regulation (GDPR) and Directive on copyright and related rights in the Digital Single Market (DSM Directive) has been building a renewed legal framework that addresses the demands of the digital economies and unseals challenges, opens prospects for further development. We examine the language resources from two perspectives. Firstly, the language resources are considered a database covered by the protection regulation (the person’s rights who created the LR database). Within the second perspective, the legal analysis focuses on the materials used for the language resource creation (data subject’s rights, copyright, related rights). The result of the research can be used for further legal investigations and policy design in the field of language technology development.KeywordsIntellectual property protectionCopyrightLanguage technologyText mining
Natural language processing (NLP) systems can turn unstructured clinic notes (e.g. progression notes, discharge summary, ECG reports) into query-able structured data to support on-demand knowledge discovery and patient care. However, processing clinical notes and extracting meaningful information is a challenging task due to ambiguity and subtlety of natural language and variations in syntactic styles. In this chapter, we performed a systematic survey starting from 1990 to 2020 and benchmarked 47 NLP publications proposed in cardiovascular domain in term of methodology developed, dataset used, and performance achieved. Finally, we performed a meta-analysis to highlight the popular trend and future direction in this field.
This book is a collection of articles accepted at the ICNLSSP 2017 conference held in Casablanca in December 2017. This conference aimed to create a synergy between different areas related to language processing: Automatic recognition, Social networks, Opinion mining, Images , Videos, ... The conference highlighted new approaches to language processing, from basic theories to their applications. ICNLSSP is an international conference dedicated to natural language processing, signal processing and speech recognition (https://isga.ma/icnlsp_web/index.php). This conference was a technical conference offering not only new research methodologies on relevant topics but also enabled the exchange of ideas between researchers from all over the world, which was very useful for doctoral students, developers and researchers in this domain.
En el contexto actual de avances tecnológicos y desarrollo de la inteligencia artificial, la digitalización de las sociedades y las mejoras tecnológicas transforman nuestras vidas en todos los ámbitos. La traducción no es una excepción. Con la aparición de la traducción automática neuronal -un nuevo paradigma de traducción automática-, la calidad que ofrece dicho sistema ha mejorado sustancialmente, incluso llegando a afirmarse que iguala o supera la calidad de la traducción humana en determinados ámbitos como las noticias. No obstante, los lenguajes de especialidad entrañan complejidades intrínsecas. En traducción jurídica, el anisomorfismo del lenguaje jurídico puede ser una brecha muy difícil de salvar para las máquinas: términos dispares para un mismo concepto en sistemas jurídicos diferentes, equivalencia cero o parcial, etc. Así, el objetivo del presente trabajo es estudiar la utilidad de la traducción automática como recurso formativo en el aula de traducción jurídica, teniendo en cuenta las características y tendencias del sector de la traducción profesional. Para ello, en este estudio se hace una evaluación humana de tres traducciones humanas de contratos societarios inglés-español y de una traducción generada por un motor de traducción automática neuronal. Los resultados apuntan a 1) que la traducción automática podría constituir una herramienta didáctica muy útil en la clase de traducción jurídica; 2) que la identificación de las competencias podría potenciarse con un enfoque de esta naturaleza; 3) la forma de incorporación de la traducción automática a la formación en Traducción Jurídica, y 4) las ventajas que tendría aquella sobre métodos de enseñanza-aprendizaje tradicionales. Palabras clave: traducción automática; tecnologías de la traducción; traducción jurídica; calidad de la traducción; evaluación de la calidad.
With the growth of online social network platforms and applications, large amounts of textual user-generated content are created daily in the form of comments, reviews, and short-text messages. As a result, users often find it challenging to discover useful information or more on the topic being discussed from such content. Machine learning and natural language processing algorithms are used to analyze the massive amount of textual social media data available online, including topic modeling techniques that have gained popularity in recent years. This paper investigates the topic modeling subject and its common application areas, methods, and tools. Also, we examine and compare five frequently used topic modeling methods, as applied to short textual social data, to show their benefits practically in detecting important topics. These methods are latent semantic analysis, latent Dirichlet allocation, non-negative matrix factorization, random projection, and principal component analysis. Two textual datasets were selected to evaluate the performance of included topic modeling methods based on the topic quality and some standard statistical evaluation metrics, like recall, precision, F-score, and topic coherence. As a result, latent Dirichlet allocation and non-negative matrix factorization methods delivered more meaningful extracted topics and obtained good results. The paper sheds light on some common topic modeling methods in a short-text context and provides direction for researchers who seek to apply these methods.
Machine translation (MT) was one of the first non-numerical applications of the computer in the 1950s and 1960s. With limited equipment and programming tools, researchers from a wide range of disciplines (electronics, linguistics, mathematics, engineering, etc.) tackled the unknown problems of language analysis and processing, investigated original and innovative methods and techniques, and laid the foundations not just of current MT systems and computerized tools for translators but also of natural language processing in general. This volume contains contributions by or about the major MT pioneers from the United States, Russia, East and West Europe, and Japan, with recollections of personal experiences, colleagues and rivals, the political and institutional background, the successes and disappointments, and above all the challenges and excitement of a new field with great practical importance. Each article includes a personal bibliography, and the editor provides an overview, chronology and list of sources for the period.
Machine translation (MT) was one of the first non-numerical applications of the computer in the 1950s and 1960s. With limited equipment and programming tools, researchers from a wide range of disciplines (electronics, linguistics, mathematics, engineering, etc.) tackled the unknown problems of language analysis and processing, investigated original and innovative methods and techniques, and laid the foundations not just of current MT systems and computerized tools for translators but also of natural language processing in general. This volume contains contributions by or about the major MT pioneers from the United States, Russia, East and West Europe, and Japan, with recollections of personal experiences, colleagues and rivals, the political and institutional background, the successes and disappointments, and above all the challenges and excitement of a new field with great practical importance. Each article includes a personal bibliography, and the editor provides an overview, chronology and list of sources for the period.
The Georgetown-IBM experiment of 1954: an evaluation in retrospect In: Papers in linguistics in honor of Dostert The Hague, Mouton (1967) ;reprinted in his On machine translation. The Hague
- P Garvin
The history of the project
- R R Macdonald
- R.R. Macdonald
Language translation by machine - a report of the first successful trial
- N Macdonald
- N. Macdonald
Automatic translation
- Yu N Panov
Is FAHQ(M)T impossible? Memoirs of Paul Garvin and other MT colleagues Early years in machine translation: memoirs and biographies of pioneers
- Christine Montgomery
Research in language translation on the IBM type 701
- P Sheridan
- P. Sheridan
The history of the project' in: General report 1952-1963
- R R R R Macdonald
- Macdonald
- Washington
Macdonald, R.R.: 'The history of the project' in: General report 1952-1963, ed. R.R.Macdonald.
Washington, DC: Georgetown University Machine Translation Project (1963), 3-17. (Georgetown
University Occasional Papers on Machine Translation, 30)
The Georgetown-I.B.M. experiment
- L E Dostert
- L.E. Dostert