Benyamin Ahmadnia

Occidental College · Department of Computer Science

In this paper we compare two different approaches for translating from Persian to Spanish, as a language pair with scarce parallel corpus. The first approach involves direct transfer using an statistical machine translation system, which is available for this language pair. The second approach involves translation through English, as a pivot langua...

Statistical Machine Translation (SMT) is making good progress in recent years. Since SMT systems are based on data-driven approach, they learn from millions or even billions of words from human-translated texts. The quality of SMT systems heavily depends on the data that we use for training step, not only its quality and amount, but also on how rel...

In this paper, we propose a sequence-to-sequence NMT model on Farsi-Spanish bilingually low-resource language pair. We apply effective preprocessing steps specific for Farsi language and optimize the model for both translation and transliteration. We also propose a loss function that enhances the word alignment and consequently improves translation...

Cybersecurity experts rely on the knowledge stored in databases like the NVD to do their work, but these are not the only sources of information about threats and vulnerabilities. Much of that information flows through social media channels. In this paper we argue that security experts and general users alike can benefit from the technologies of th...

Although the Neural Machine Translation (NMT) framework has already been shown effective in large training data scenarios, it is less effective for low-resource conditions. To improve NMT performance in a low-resource setting, we extend the high-quality training data by generating a pseudo bilingual dataset and then filtering out low-quality alignm...

This paper describes a systematic study of an approach to Farsi-Spanish low-resource Neural Machine Translation (NMT) that leverages monolingual data for joint learning of forward and backward translation models. As is standard for NMT systems, the training process begins using two pre-trained translation models that are iteratively updated by decr...

Neural Machine Translation (NMT) performs training of a neural network employing an encoder-decoder architecture. However, the quality of the neural-based translations predominantly depends on the availability of a large amount of bilingual training dataset. In this paper, we explore the performance of translations predicted by attention-based NMT...

In this paper, we propose a useful optimization method for low-resource Neural Machine Translation (NMT) by investigating the effectiveness of multiple neural network optimization algorithms. Our results confirm that applying the proposed optimization method on English-Persian translation can exceed translation quality compared to the English-Persi...

An important prerequisite for data-driven Machine Translation (MT) systems is the availability of high-quality training data. Corpus-based MT systems requiring domain specificity additionally require the selection of a large training dataset and application of proper domain adaptation techniques. This paper demonstrates the portability of MT system...

Neural Machine Translation (NMT) systems require a massive amount of Maintaining semantic relations between words during the translation process yields more accurate target-language output from Neural Machine Translation (NMT). Although difficult to achieve from training data alone, it is possible to leverage Knowledge Graphs (KGs) to retain source...

The quality of data-driven Machine Translation (MT) strongly depends on the quantity as well as the quality of the training dataset. However, collecting a large set of training parallel texts is not easy in practice. Although various approaches have already been proposed to overcome this issue, the lack of large parallel corpora still poses a major...

Neural Machine Translation (NMT) relies heavily on word embeddings, which are continuous representations of words in a vector space, obtained from large monolingual data and, independently, from bilingual data for NMT model training. Word embeddings have proven to be invaluable for performance improvements in natural language analysis tasks that ot...

The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. Generally, the NMT systems learn from millions of words from...

The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. This paper describes a round-trip training approach to bilin...

Phrases play a key role in Machine Translation (MT). In this paper, we apply a Long Short-Term Memory (LSTM) model over conventional Phrase-Based Statistical MT (PBSMT). The core idea is to use an LSTM encoder-decoder to score the phrase table generated by the PBSMT decoder. Given a source sequence, the encoder and decoder are jointly trained in or...

This paper investigates the idea of making effective use of bridge language techniques to respond to minimal parallel-resource data set bottleneck reality to improve translation quality in the case of Persian-Spanish low-resource language pair using a well-resourced language such as English as the bridge one. We apply the optimized direct-bridge co...

In this paper, we apply the round-tripping algorithm to Statistical Machine Translation (SMT) for making effective use of monolingual data to tackle the training data scarcity. In this approach, the outbound-trip (forward) and inbound-trip (backward) translation tasks make a closed-loop and produce informative feedback to train the translation mode...

The quality of Neural Machine Translation (NMT) systems like Statistical Machine Translation (SMT) systems, heavily depends on the size of training data set, while for some pairs of languages, high-quality parallel data are poor resources. In order to respond to this low-resourced training data bottleneck reality, we employ the pivoting approach in...

This paper is an attempt to exclusively focus on investigating the pivot language technique in which a bridging language is utilized to increase the quality of the Persian–Spanish low-resource Statistical Machine Translation (SMT). In this case, English is used as the bridging language, and the Persian–English SMT is combined with the English–Spani...

Abstract: Statistical machine translation is a method that automatically acquires knowledge from large amounts of training data. There are some approaches in order to train a statistical machine translation system such as word-based, phrase-based, syntax-based, and hierarchical phrase-based. In this paper we compare a classical phrase-based transla...