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Generating Chinese Classical Poems with RNN Encoder-Decoder

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Abstract

We take the generation of Chinese classical poem lines as a sequence-to-sequence learning problem, and build a novel system based on the RNN Encoder-Decoder structure to generate quatrains (Jueju in Chinese), with a topic word as input. Our system can jointly learn semantic meaning within a single line, semantic relevance among lines in a poem, and the use of structural, rhythmical and tonal patterns, without utilizing any constraint templates. Experimental results show that our system outperforms other competitive systems. We also find that the attention mechanism can capture the word associations in Chinese classical poetry and inverting target lines in training can improve performance.

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... Most approaches employ rules or templates (Tosa et al., 2008;Wu et al., 2009;Netzer et al., 2009;Oliveira, 2009;Oliveira, 2012), genetic algorithms (Manurung, 2004;Zhou et al., 2010;Manurung et al., 2012), summarization methods (Yan et al., 2013) and statistical machine translation methods (Jiang and Zhou, 2008;He et al., 2012) to generate poems. More recently, deep learning methods have emerged as a promising discipline, which considers the poetry generation as a sequence-to-sequence generation problem (Zhang and Lapata, 2014;Wang et al., 2016;Yi et al., 2016). These methods usually generate the first line by selecting one line from the dataset of poems according to the user's writing intents (usually a set of keywords), and the other three lines are generated based on the first line and the previous lines. ...
... Therefore, the first line is provided by users and must be a well-written sentence of the poem. Yi et al. (2016) extended this approach to generate Chinese quatrains. The problem of generating the first line is resolved by a separate neural machine translation (NMT) model which takes one keyword as input and translates it into the first line. ...
... The previous methods can only support some keywords or must provide the first line. Second, we use planning-based method to determine the topic of the poem according to the user's input, with each line having one specific sub-topic, which guarantees that the generated poem is coherent and well organized, therefore avoiding the problem of the previous method that only the first line is guaranteed to be related to the user's intent while the next lines may be irrelevant with the intention due to the coherent decay problem (He et al., 2012;Zhang and Lapata, 2014;Wang et al., 2016;Yi et al., 2016). Third, the rhythm or tone in (Zhou et al., 2010;Yan et al., 2013;Zhang and Lapata, 2014;Yi et al., 2016;Marjan Ghazvininejad and Knight, 2016) is controlled by rules or extra structures, while our model can automatically learn constrains from the training corpus. ...
Article
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Chinese poetry generation is a very challenging task in natural language processing. In this paper, we propose a novel two-stage poetry generating method which first plans the sub-topics of the poem according to the user's writing intent, and then generates each line of the poem sequentially, using a modified recurrent neural network encoder-decoder framework. The proposed planning-based method can ensure that the generated poem is coherent and semantically consistent with the user's intent. A comprehensive evaluation with human judgments demonstrates that our proposed approach outperforms the state-of-the-art poetry generating methods and the poem quality is somehow comparable to human poets.
... Zhang and Lapata [19] compressed all previous information into a vector to guide the generation. Wang et al. [13,14] and Yi et al. [17] use bidirectional recurrent neural network (RNN) with attention mechanism to help capture long time dependency between poetry lines. Wang et al. [15] proposed a planning-based method to ensure the coherence of generated poems. ...
... It is not suitable to deal with quatrains which have four lines. To overcome this issue, we adopt bidirectional Long Short Term Memory (LSTM) model for the encoder and another LSTM for the decoder [13][14][15]17]. In this way, the encoder-decoder model can well capture long distance patterns. ...
... A word-to-line module takes a word as input and then ouput a line, but a line-to-line module produces a line according to one line or multi-lines. Yi et al. [17] proposed a three-block system to generate a whole quatrain that was reported to work well in learning the rhyme automatically. However, the structure seems too complicated (four separate models in total). ...
Conference Paper
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We propose a novel text steganography method using RNN Encoder-Decoder structure to generate quatrains, one genre of Chinese poetry. Compared to other text-generation based steganography methods which have either very low embedding rate or flaws in the naturalness of generated texts, our method has higher embedding rate and better text quality. In this paper, we use the LSTM Encoder-Decoder model to generate the first line of a quatrain with a keyword and then generate the following lines one by one. RNN has proved effective in generating poetry, but when applied to steganograpy, poetry quality decreases sharply, because of the redundancy we create to hide information. To overcome this problem, we propose a template-constrained generation method and develop a word-choosing approach using inner-word mutual information. Through a series of experiments, it is proven that our approach outperforms other poetry steganography methods in both embedding rate and poetry quality.
... Among them, rule-based approaches (Tosa, Obara, and Minoh 2008;Wu, Tosa, and Nakatsu 2009;Netzer et al. 2009;Oliveira 2009;, genetic algorithms (Manurung 2004;Zhou, You, and Ding 2010;Manurung, Ritchie, and Thompson 2012) and statistical machine translation methods (Jiang and Zhou 2008;He, Zhou, and Jiang 2012) have been developed. More recently, with the significant advances in deep neural networks, a number of poetry generation algorithms based on neural networks have been proposed with the paradigm of sequence-to-sequence learning, where poems are generated line by line and each line is generated by taking the previous lines as input (Zhang and Lapata 2014;Yi, Li, and Sun 2016;Wang et al. 2016a;2016b;Zhang et al. 2017). However, restrictions exist in previous works, including topic drift and semantic inconsistency which are caused by only considering the writing intent of a user in the first line. ...
... An RNN-based framework is proposed in (Zhang and Lapata 2014) where each poem line is generated by taking the previously generated lines as input. In the work of (Yi, Li, and Sun 2016), the attention mechanism is introduced into poetry generation, where an attention-based Encoder-Decoder model is proposed to generate poem lines sequentially. A different genre of classical Chinese poetry is generated in (Wang et al. 2016a), which is the first work to generate Chinese Song iambics, with each line of variable length. ...
... The model is trained with the AdaDelta algorithm (Zeiler 2012) with the batch size set to 128, and the final model is selected according to the cross entropy loss on the validation set. During training, we invert each line to be generated following (Yi, Li, and Sun 2016) to make it easier for the model to generate poems obeying rhythm rules. For the visual feature extractor, we choose a pre-trained VGG-19 (Simonyan and Zisserman 2014) model and use the output of the conv5 4 layer, which includes 196 vectors of 512 dimensions, as the local visual features of an image. ...
Article
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With the recent advances of neural models and natural language processing, automatic generation of classical Chinese poetry has drawn significant attention due to its artistic and cultural value. Previous works mainly focus on generating poetry given keywords or other text information, while visual inspirations for poetry have been rarely explored. Generating poetry from images is much more challenging than generating poetry from text, since images contain very rich visual information which cannot be described completely using several keywords, and a good poem should convey the image accurately. In this paper, we propose a memory based neural model which exploits images to generate poems. Specifically, an Encoder-Decoder model with a topic memory network is proposed to generate classical Chinese poetry from images. To the best of our knowledge, this is the first work attempting to generate classical Chinese poetry from images with neural networks. A comprehensive experimental investigation with both human evaluation and quantitative analysis demonstrates that the proposed model can generate poems which convey images accurately.
... As neural network demonstrates powerful ability for natural language representation (Bengio et al. 2003;Goldberg 2017), different neural network structures have been utilized for poem generation and shown great advances. The main structures are developed from vanilla recurrent neural network (Zhang and Lapata 2014) to bi-directional long short term memory network and bi-directional gated recurrent unit network Yi, Li, and Sun 2017). The poem generation is widely interpreted as a sequence-tosequence problem, which utilize the encoder-decoder framework to encode the previous sequence and generate the later sequence with the decoder (Wang, Luo, and Wang 2016;Yi, Li, and Sun 2017). ...
... The main structures are developed from vanilla recurrent neural network (Zhang and Lapata 2014) to bi-directional long short term memory network and bi-directional gated recurrent unit network Yi, Li, and Sun 2017). The poem generation is widely interpreted as a sequence-tosequence problem, which utilize the encoder-decoder framework to encode the previous sequence and generate the later sequence with the decoder (Wang, Luo, and Wang 2016;Yi, Li, and Sun 2017). To strengthen the relation between the encoder and decoder, the attention mechanism has been incorporated for poem generation (Wang, Luo, and Wang 2016;Zhang et al. 2017;Yi, Li, and Sun 2017). ...
... The poem generation is widely interpreted as a sequence-tosequence problem, which utilize the encoder-decoder framework to encode the previous sequence and generate the later sequence with the decoder (Wang, Luo, and Wang 2016;Yi, Li, and Sun 2017). To strengthen the relation between the encoder and decoder, the attention mechanism has been incorporated for poem generation (Wang, Luo, and Wang 2016;Zhang et al. 2017;Yi, Li, and Sun 2017). Besides, some tricks like working memory mechanism and salientclue mechanism have been proposed to improve the coherence in meanings and topics for poem generation. ...
Article
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Owing to its unique literal and aesthetical characteristics, automatic generation of Chinese poetry is still challenging in Artificial Intelligence, which can hardly be straightforwardly realized by end-to-end methods. In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation. In the first stage, an encoder-decoder structure is utilized to generate a poem draft. Afterwards, our proposed Quality-Aware Masked Language Model (QA-MLM) is employed to polish the draft towards higher quality in terms of linguistics and literalness. Based on a multi-task learning scheme, QA-MLM is able to determine whether polishing is needed based on the poem draft. Furthermore, QA-MLM is able to localize improper characters of the poem draft and substitute with newly predicted ones accordingly. Benefited from the masked language model structure, QA-MLM incorporates global context information into the polishing process, which can obtain more appropriate polishing results than the unidirectional sequential decoding. Moreover, the iterative polishing process will be terminated automatically when QA-MLM regards the processed poem as a qualified one. Both human and automatic evaluation have been conducted, and the results demonstrate that our approach is effective to improve the performance of encoder-decoder structure.
... In [13] it was shown that comparable prosaic texts can be generated with LSTM networks as well. There are a number of works specifically focused on Chinese classical poetry generation, for example [14], [15], [16], [17], or [18], however interesting contributions in the area of generative poetry in languages other than Chinese or in a multilingual setting are relatively rare. One could mention the paper by [19] where an algorithm generates a poem in line with a topic given by the user and the paper by [20] in which the authors generate stylized rap lyrics with LSTM trained on a rap poetry corpus. ...
... Some cultures and styles also have particular semantic limitations or 'standards', for example, 'centrality' of certain topics in classical Japanese poetry, see [39]. We do not make attempts to address high-level semantic structure, however one can add some kind of pseudosemantic rules to our model, say via some mechanism in line with [19] or [15]. Instead, we suggest focusing on syntax and phonetics. ...
Preprint
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This paper addresses the problem of stylized text generation in a multilingual setup. A version of a language model based on a long short-term memory (LSTM) artificial neural network with extended phonetic and semantic embeddings is used for stylized poetry generation. Phonetics is shown to have comparable importance for the task of stylized poetry generation as the information on the target author. The quality of the resulting poems generated by the network is estimated through bilingual evaluation understudy (BLEU), a survey and a new cross-entropy based metric that is suggested for the problems of such type. The experiments show that the proposed model consistently outperforms random sample and vanilla-LSTM baselines, humans also tend to attribute machine generated texts to the target author.
... They force the decoder to rhyme the second and fourth lines, trusting the RNN to control rhythm. Yi et al. (2016) also propose an attentionbased bidirectional RNN model for generating 4- line Chinese poems. The only such work which tries to generate longer poems is from Wang et al. (2016), who use an attention-based LSTM model for generation iambic poems. ...
... in a poetry corpus, then use these in a finite-state network to generate short English love poems. Several deep learning methods have recently been proposed for generating poems. Zhang and Lapata (2014) use an RNN model to generate 4-line Chinese poems. They force the decoder to rhyme the second and fourth lines, trusting the RNN to control rhythm.Yi et al. (2016)also propose an attentionbased bidirectional RNN model for generating 4line Chinese poems. The only such work which tries to generate longer poems is fromWang et al. (2016), who use an attention-based LSTM model for generation iambic poems. They train on a small dataset and do not use an explicit system for constraining rhythm and rhyme ...
... Some cultures and styles also have particular semantic limitations or 'standards', for example, 'centrality' of certain topics in classical Japanese poetry, see (Maynard, 1994). We do not make attempts to address high-level semantic structure, however one can add some kind of pseudo-semantic rules to the model discussed further, say via some mechanism in line with (Ghazvininejad et al., 2016) or (Yi et al., 2017). The importance of phonetics in poetical texts was broadly discussed among Russian futuristic poets, see (Kruchenykh, 1923). ...
... In (Sutskever et al., 2011) and in (Graves, 2013) it is demonstrated that a character-based recurrent neural network with gated connections or LSTM networks respectively can generate texts that resemble news or Wikipedia articles. Chinese classical poetry due to its diverse and deeply studied structure is addressed in (He et al., 2012), (Yi et al., 2017), (Yan, 2016), (Yan et al., 2016), or (Zhang et al., 2017). In (Ghazvininejad et al., 2016) an algorithm generates a poem in line with a user-defined topic in (Potash et al., 2015) stylized rap lyrics are generated with LSTM trained on a rap poetry corpus. ...
... Developing machines that reproduce artistic behaviours and learn to be creative is a long-standing goal of the scientific community in the context of Artificial Intelligence [1,2]. Recently, several researches focused on the case of the noble art of Poetry, motivated by success of Deep Learning approaches to Natural Language Processing (NLP) and, more specifically, to Natural Language Generation [3,4,5,6,7,8]. However, existing Machine Learning-based poem generators do not model the natural way poems are created by humans, i.e., poets usually do not create their compositions all in one breath. ...
... In order to show the quality of the detector module and that approaching text correction as shortest path problem is feasible, we created "corrupted" poems from real poems in the dataset by replacing one or more words in random positions with words sampled from the entire vocabulary V . 5 The agent operates in an environment where each episode starts with a corrupted poem, and it has to learn to reconstruct the original not-corrupted poem, selecting at each step which word to change. In this artificial setting we assume that, once the agent picks which word to substitute, a perfect prompter (oracle) will replace it with the ground truth, i.e. the word originally positioned there in the real poem. ...
Preprint
Writers, poets, singers usually do not create their compositions in just one breath. Text is revisited, adjusted, modified, rephrased, even multiple times, in order to better convey meanings, emotions and feelings that the author wants to express. Amongst the noble written arts, Poetry is probably the one that needs to be elaborated the most, since the composition has to formally respect predefined meter and rhyming schemes. In this paper, we propose a framework to generate poems that are repeatedly revisited and corrected, as humans do, in order to improve their overall quality. We frame the problem of revising poems in the context of Reinforcement Learning and, in particular, using Proximal Policy Optimization. Our model generates poems from scratch and it learns to progressively adjust the generated text in order to match a target criterion. We evaluate this approach in the case of matching a rhyming scheme, without having any information on which words are responsible of creating rhymes and on how to coherently alter the poem words. The proposed framework is general and, with an appropriate reward shaping, it can be applied to other text generation problems.
... Yi et al. (Yi et al., 2017) based their work on a sequence-to-sequence model (Cho et al., 2014) to generate Chinese poems. They built an encoderdecoder framework based on a bi-directional recurrent neural network (Bi-RNN) with an attention mechanism. ...
Conference Paper
Full-text available
Automatically generating poetry by computers is a challenging topic that requires the use of advanced deep learning techniques. While much attention has been given to English and Chinese poem generation, there are few significant efforts considering other languages. Generating poems in Arabic is a difficult task due to the complexity of the Arabic language grammatical structure. In this paper, we investigate the feasibility of training generative pre-trained language model GPT-2 to generate Arabic poems. The results of the experiments, which included the BLEU score as well as human assessments, confirmed the effectiveness of our proposed model. Both automatic and human evaluations show that our proposed model outperforms existing models in generating Arabic poetry.
... The model generates the first line using the keywords given by users, and the other lines are generated according to the first line. [13], [16], [20] use an Encoder-Decoder model with attention mechanism to generate poetry. Moreover, this model applies to the generation of Chinese Song iambics [12], which is a different genre in Chinese classical literature. ...
Preprint
In recent years, the automatic generation of classical Chinese poetry has made great progress. Besides focusing on improving the quality of the generated poetry, there is a new topic about generating poetry from an image. However, the existing methods for this topic still have the problem of topic drift and semantic inconsistency, and the image-poem pairs dataset is hard to be built when training these models. In this paper, we extract and integrate the Concrete and Abstract information from images to address those issues. We proposed an infilling-based Chinese poetry generation model which can infill the Concrete keywords into each line of poems in an explicit way, and an abstract information embedding to integrate the Abstract information into generated poems. In addition, we use non-parallel data during training and construct separate image datasets and poem datasets to train the different components in our framework. Both automatic and human evaluation results show that our approach can generate poems which have better consistency with images without losing the quality.
... Although we know of a growing number of automatic poetry generation studies Oliveira (2012); He et al (2012); Yan et al (2013); Zhang and Lapata (2014); Yi et al (2016); Wang et al (2016); Hopkins and Kiela (2017); Oliveira (2017), to date there are just three publications existing in the field of automatic poetry translation. Greene et al (2010) used a phrase-based machine translation approach to translate Italian poems to English translation lattices, searching these lattices for the best translation that obeys a given metrical pattern. ...
Article
Full-text available
The translation of poetry is a complex, multifaceted challenge: the translated text should communicate the same meaning, similar metaphoric expressions, and also match the style and prosody of the original poem. Research on machine poetry translation is existing since 2010, but for four reasons it is still rather insufficient: 1) The few approaches existing completely lack any knowledge about current developments in both lyric theory and translation theory. 2) They are based on very small datasets. 3) They mostly ignored the neural learning approach that superseded the long-standing dominance of phrase-based approaches within machine translation. 4) They have no concept concerning the pragmatic function of their research and the resulting tools. Our paper describes how to improve the existing research and technology for poetry translations in exactly these four points. With regards to 1) we will describe the ‘Poetics of Translation’. With regards to 2) we will introduce the Worlds largest corpus for poetry translations from lyrikline. With regards to 3) we will describe first steps towards a neural machine translation of poetry.With regards to 4) we will describe first steps towards the development of a poetry translation mapping system.
... is widely used to generate poems that can even confuse readers from telling them from poems written by human poets [8,9,12,37,41]. Previous works of poem generation mainly focus on style and rhythmic qualities of poems [12,35], while recent studies introduce topic as a condition for poem generation [8,9,35,41]. For a poem, topic is still a rather abstract concept without specific scenarios. ...
Conference Paper
Full-text available
Automatic generation of natural language from images has attracted extensive attention. In this paper, we take one step further to investigate generation of poetic language (with multiple lines) to an image for automatic poetry creation. This task involves multiple challenges, including discovering poetic clues from the image (e.g., hope from green), and generating poems to satisfy both relevance to the image and poeticness in language level. To solve the above challenges, we formulate the task of poem generation into two correlated sub-tasks by multi-adversarial training via policy gradient, through which the cross-modal relevance and poetic language style can be ensured. To extract poetic clues from images, we propose to learn a deep coupled visual-poetic embedding, in which the poetic representation from objects, sentiments \footnoteWe consider both adjectives and verbs that can express emotions and feelings as sentiment words in this research. and scenes in an image can be jointly learned. Two discriminative networks are further introduced to guide the poem generation, including a multi-modal discriminator and a poem-style discriminator. To facilitate the research, we have released two poem datasets by human annotators with two distinct properties: 1) the first human annotated image-to-poem pair dataset (with $8,292$ pairs in total), and 2) to-date the largest public English poem corpus dataset (with $92,265$ different poems in total). Extensive experiments are conducted with 8K images, among which 1.5K image are randomly picked for evaluation. Both objective and subjective evaluations show the superior performances against the state-of-the-art methods for poem generation from images. Turing test carried out with over $500$ human subjects, among which 30 evaluators are poetry experts, demonstrates the effectiveness of our approach.
... By applying deep learning approaches recent years, researches about poetry generation has entered a new stage. Recur-rent neural network is widely used to generate poems that can even confuse readers from telling them from poems written by human poets [7,8,11,33,37]. Previous works of poem generation mainly focus on style and rhythmic qualities of poems [11,32], while recent studies introduce topic as a condition for poem generation [7,8,32,37]. For a poem, topic is still a rather abstract concept without specific scenarios. ...
Article
Full-text available
Automatic generation of natural language from images has attracted extensive attention. In this paper, we take one step further to investigate generation of poetic language (with multiple lines) to an image for automatic poetry creation. This task involves multiple challenges, including discovering poetic clues from the image (e.g., hope from green), and generating poems to satisfy both relevance to the image and poeticness in language level. To solve the above challenges, we formulate the task of poem generation into two correlated sub-tasks by multi-adversarial training via policy gradient, through which the cross-modal relevance and poetic language style can be ensured. To extract poetic clues from images, we propose to learn a deep coupled visual-poetic embedding, in which the poetic representation from objects, sentiments and scenes in an image can be jointly learned. Two discriminative networks are further introduced to guide the poem generation, including a multi-modal discriminator and a poem-style discriminator. To facilitate the research, we have collected two poem datasets by human annotators with two distinct properties: 1) the first human annotated image-to-poem pair dataset (with 8,292 pairs in total), and 2) to-date the largest public English poem corpus dataset (with 92,265 different poems in total). Extensive experiments are conducted with 8K images generated with our model, among which 1.5K image are randomly picked for evaluation. Both objective and subjective evaluations show the superior performances against the state-of-art methods for poem generation from images. Turing test carried out with over 500 human subjects, among which 30 evaluators are poetry experts, demonstrates the effectiveness of our approach.
... (Wang et al. 2016a) applied the attention-based model. (Yi, Li, and Sun 2016) extended the approach into a quatrain generator with an input word as a topic. (Ghazvininejad et al. 2016) generated poems on a user-supplied topic with rhythmic and rhyme constraints. ...
Preprint
Full-text available
Vision is a common source of inspiration for poetry. The objects and the sentimental imprints that one perceives from an image may lead to various feelings depending on the reader. In this paper, we present a system of poetry generation from images to mimic the process. Given an image, we first extract a few keywords representing objects and sentiments perceived from the image. These keywords are then expanded to related ones based on their associations in human written poems. Finally, verses are generated gradually from the keywords using recurrent neural networks trained on existing poems. Our approach is evaluated by human assessors and compared to other generation baselines. The results show that our method can generate poems that are more artistic than the baseline methods. This is one of the few attempts to generate poetry from images. By deploying our proposed approach, XiaoIce has already generated more than 12 million poems for users since its release in July 2017. A book of its poems has been published by Cheers Publishing, which claimed that the book is the first-ever poetry collection written by an AI in human history.
... In spite of recent works in automatic poetry generation (Oliveira, 2012;He et al., 2012;Yan et al., 2013;Zhang and Lapata, 2014;Yi et al., 2017;Wang et al., 2016;Ghazvininejad et al., 2016Ghazvininejad et al., , 2017Hopkins and Kiela, 2017;Oliveira, 2017), little has been done on automatic poetry translation. Greene et al. (2010) use phrase-based machine translation techniques to translate Italian poetic lines to English-translation lattices. ...
... Apart from this, the approach has been applied to morphological inflection (Kann and Schütze, 2016;Cotterell et al., 2016;Östling and Bjerva, 2017;Cotterell et al., 2017), AMR parsing (Barzdins and Gosko, 2016;Konstas et al., 2017;van Noord and Bos, 2017b,a), language modelling (e.g. Vinyals et al., 2015), generation of Chinese poetry (Yi et al., 2016), historical text normalisation (Korchagina, 2017), and a whole host of other tasks. ...
Thesis
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When learning a new skill, you take advantage of your preexisting skills and knowledge. For instance, if you are a skilled violinist, you will likely have an easier time learning to play cello. Similarly, when learning a new language you take advantage of the languages you already speak. For instance, if your native language is Norwegian and you decide to learn Dutch, the lexical overlap between these two languages will likely benefit your rate of language acquisition. This thesis deals with the intersection of learning multiple tasks and learning multiple languages in the context of Natural Language Processing (NLP), which can be defined as the study of computational processing of human language. Although these two types of learning may seem different on the surface, we will see that they share many similarities. The traditional approach in NLP is to consider a single task for a single language at a time. However, recent advances allow for broadening this approach, by considering data for multiple tasks and languages simultaneously. This is an important approach to explore further as the key to improving the reliability of NLP, especially for low-resource languages, is to take advantage of all relevant data whenever possible. In doing so, the hope is that in the long term, low-resource languages can benefit from the advances made in NLP which are currently to a large extent reserved for high-resource languages. This, in turn, may then have positive consequences for, e.g., language preservation, as speakers of minority languages will have a lower degree of pressure to using high-resource languages. In the short term, answering the specific research questions posed should be of use to NLP researchers working towards the same goal.
... Automated poetry generation is attracting increasing research effort. Researchers approach the problem by using grammatical and semantic templates (Oliveira, 2009(Oliveira, , 2012 or treating the generation task as a translation/summarization task (Zhou et al., 2009;He et al., 2012;Yan et al., 2013;Zhang and Lapata, 2014;Yi et al., 2016;Wang et al., 2016;Ghazvininejad et al., 2016). However, such poetry generation systems face these challenges: ...
... Fol- lowing the approaches in SMT framework, it is valid to regard the metaphors with its radicals as the align- ments. There are several works using neural network to generate Chinese poems( Zhang and Lapata, 2014;Yi et al., 2016). Due to the limited data and strict rules, it is hard to transfer to the riddle generation. ...
... Human evaluation is necessary for poetry generation. In order to make our results more believable, we use four criteria for human evaluation following Yi et al. [37] and Wang et al. [25]: Fluency: it measures whether the poem reads smoothly and fluently. Coherence: it measures the relevance of adjacent lines in one poem. ...
Preprint
Chinese poetry is an important part of worldwide culture, and classical and modern sub-branches are quite different. The former is a unique genre and has strict constraints, while the latter is very flexible in length, optional to have rhymes, and similar to modern poetry in other languages. Thus, it requires more to control the coherence and improve the novelty. In this paper, we propose a generate-retrieve-then-refine paradigm to jointly improve the coherence and novelty. In the first stage, a draft is generated given keywords (i.e., topics) only. The second stage produces a "refining vector" from retrieval lines. At last, we take into consideration both the draft and the "refining vector" to generate a new poem. The draft provides future sentence-level information for a line to be generated. Meanwhile, the "refining vector" points out the direction of refinement based on impressive words detection mechanism which can learn good patterns from references and then create new ones via insertion operation. Experimental results on a collected large-scale modern Chinese poetry dataset show that our proposed approach can not only generate more coherent poems, but also improve the diversity and novelty.
... Previous work on handling the shortcomings of MLE include length-normalizing sentence probability [6], future cost estimation [7], diversity-boosting objective function [8,2] or penalizing repeating tokens [9]. When it comes to poetry generation using generative text models, Zhang and Lapata [10], Yi et al. [11] and Wang et al. [12] use language modeling to generate Chinese poems. However, none of these methods provide feedback on the quality of the generated sample and hence, do not address the qualitative objective required for creative decoding. ...
Preprint
Generative models for text have substantially contributed to tasks like machine translation and language modeling, using maximum likelihood optimization (MLE). However, for creative text generation, where multiple outputs are possible and originality and uniqueness are encouraged, MLE falls short. Methods optimized for MLE lead to outputs that can be generic, repetitive and incoherent. In this work, we use a Generative Adversarial Network framework to alleviate this problem. We evaluate our framework on poetry, lyrics and metaphor datasets, each with widely different characteristics, and report better performance of our objective function over other generative models.
... Most recently, the automatic poetry generation has received great attention, neural networks have made this task a great development, including recurrent neural network (Zhang and Lapata 2014), encoder-decoder sequence to sequence model (Yi, Li, and Sun 2017), and neural attentionbased model (Wang et al. 2016), etc. These studies are of great significance to entertainment and education. ...
Article
In this work, we demonstrate a Chinese classical poetry generation system called Deep Poetry. Existing systems for Chinese classical poetry generation are mostly template-based and very few of them can accept multi-modal input. Unlike previous systems, Deep Poetry uses neural networks that are trained on over 200 thousand poems and 3 million ancient Chinese prose. Our system can accept plain text, images or artistic conceptions as inputs to generate Chinese classical poetry. More importantly, users are allowed to participate in the process of writing poetry by our system. For the user's convenience, we deploy the system at the WeChat applet platform, users can use the system on the mobile device whenever and wherever possible.
... All the neural models mentioned above attempt to generate fluent and meaningful poems with rules in rhymes and tones, which can be learned from corpus patterns but there were no considerations on improving contexts throughout the poem generated. Most recently, researchers have suggested many new methods of poetry generation, such as employ memory networks [18] [19], Variational AutoEncoder (VAE) [20] and reinforcement learning [21]. Related to our work, a transformer-based language model, GPT2 [3] transformerbased language model is proven to be good at natural language generation, which performs very well on casual texts with context controlling. ...
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Automatic text generation has garnered growing attention in recent years as an essential step towards computer creativity. Generative Pretraining Transformer 2 (GPT2) is one of the state of the art approaches that have excellent successes. In this paper, we took the first step to investigate the power of GPT2 in traditional Vietnamese poetry generation. In the earlier time, our experiment with base GPT2 was quite good at generating the poem in the proper template. Though it can learn the patterns, including rhyme and tone rules, from the training data, like almost all other text generation approaches, the poems generated still has a topic drift and semantic inconsistency. To improve the cohesion within the poems, we proposed a new model SP-GPT2 (semantic poem GPT2) which was built on the top GPT2 model and an additional loss to constrain context throughout the entire poem. For better evaluation, we examined the methods by both automatic quantitative evaluation and human evaluation. Both automatic and human evaluation demonstrated that our approach can generate poems that have better cohesion without losing the quality due to additional loss. At the same time, we are the pioneers of this topic. We released the first computational scoring module for poems generated in the template containing the style rule dictionary. Additionally, we are the first to publish a Luc-Bat dataset, including 87609 Luc Bat poems, which is equivalent to about 2.6 million sentences, combined with about 83579 poems in other styles was also published for further exploration. The code is available at https://github.com/fsoft-ailab/Poem-Generator
... Generative poetry dates back as far as (Wheatley 1965) along with other early generative mechanisms and has various subfields at the moment. Generation of poems could be addressed following specific literary tradition, see (He, Zhou, and Jiang 2012;Yan et al. 2016;Yi, Li, and Sun 2017); could be focused on the generation of topical poetry (Ghazvininejad et al. 2016); could be centered around stylization that targets a certain author or a genre (Potash, Romanov, and Rumshisky 2015). For a taxonomy of generative poetry techniques, we address the reader to (Lamb, Brown, and Clarke 2017). ...
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This papers revisits the receptive theory in context of computational creativity. It presents a case study of a Paranoid Transformer - a fully autonomous text generation engine with raw output that could be read as the narrative of a mad digital persona without any additional human post-filtering. We describe technical details of the generative system, provide examples of output and discuss the impact of receptive theory, chance discovery and simulation of fringe mental state on the understanding of computational creativity.
... They compressed the previously generated Tang poetry into an input vector to guide the generation of the next Tang poetry, which was similar to the task of machine translation. Wang et al. [7,8] proposed a method to generate Tang poetry with a consistent theme using a recurrent neural network (RNN) with an attention mechanism. In the process of training and generating Tang poetry, this method focused on the important parts of each poem and the sentences with strong dependence, thereby improving the thematic consistency of all the Tang poems. ...
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... Human evaluation is necessary for poetry generation. In order to make our results more believable, we use four criteria for human evaluation following Yi et al. [37] and Wang et al. [25]: Fluency: it measures whether the poem reads smoothly and fluently. Coherence: it measures the relevance of adjacent lines in one poem. ...
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... First, similar to Yi et al. (2017), we could follow line-to-line poem generation, where the network takes the previous line as prompt and generates a new line which, in turn, is the prompt for the next entry. We could also experiment with different architectures, such as Transformer (Vaswani et al., 2017), as well as training schemes. ...
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This paper proposes a generative language model called AfriKI. Our approach is based on an LSTM architecture trained on a small corpus of contemporary fiction. With the aim of promoting human creativity, we use the model as an authoring tool to explore machine-in-the-loop Afrikaans poetry generation. To our knowledge, this is the first study to attempt creative text generation in Afrikaans.
... Generative poetry dates back as far as the work of Wheatley [13], along with other early generative mechanisms, and has various subfields at the moment. Generation of poems could be centered around specific literary tradition (see [14][15][16]); could be focused on the generation of topical poetry [17]; or could be centered around stylization that targets a certain author [18] or a genre [19]. For a taxonomy of generative poetry techniques, we address the reader to the work of Lamb et al. [20]. ...
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This paper revisits the receptive theory in the context of computational creativity. It presents a case study of a Paranoid Transformer—a fully autonomous text generation engine with raw output that could be read as the narrative of a mad digital persona without any additional human post-filtering. We describe technical details of the generative system, provide examples of output, and discuss the impact of receptive theory, chance discovery, and simulation of fringe mental state on the understanding of computational creativity.
... First, similar to Yi et al. (2017), we could follow line-to-line poem generation, where the network takes the previous line as prompt and generates a new line which, in turn, is the prompt for the next entry. We could also experiment with different architectures, such as Transformer (Vaswani et al., 2017), as well as training schemes. ...
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This paper proposes a generative language model called AfriKI. Our approach is based on an LSTM architecture trained on a small corpus of contemporary fiction. With the aim of promoting human creativity, we use the model as an authoring tool to explore machine-in-the-loop Afrikaans poetry generation. To our knowledge, this is the first study to attempt creative text generation in Afrikaans.
Chapter
Chinese classical poetry generation from images is an overwhelmingly challenging work in the field of artificial intelligence. Inspired by recent advances in automatically generating description of an image and Chinese poem generation, in this paper, we present a generative model based on deep recurrent framework that describes images in the form of poems. Our model consists of two parts, one is to extract information according to the semantics presented in images, and the other is to generate each line of the poem incrementally according to the extracted semantic information from the images by a recurrent neural network. Experimental results thoroughly demonstrate the effectiveness of our approach by manual evaluation.
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Chapter
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Chapter
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We provide code that produces beautiful poetry. Our sonnet-generation algorithm includes several novel elements that improve over the state-of-the-art, leading to rhythmic and inspiring poems. The work discussed here is the winner of the 2018 PoetiX Literary Turing Test Award for computer-generated poetry.
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This paper presents a novel, syllable-structured Chinese lyrics generation model given a piece of original melody. Most previously reported lyrics generation models fail to include the relationship between lyrics and melody. In this work, we propose to interpret lyrics-melody alignments as syllable structural information and use a multi-channel sequence-to-sequence model with considering both phrasal structures and semantics. Two different RNN encoders are applied, one of which is for encoding syllable structures while the other for semantic encoding with contextual sentences or input keywords. Moreover, a large Chinese lyrics corpus for model training is leveraged. With automatic and human evaluations, results demonstrate the effectiveness of our proposed lyrics generation model. To the best of our knowledge, there is few previous reports on lyrics generation considering both music and linguistic perspectives.
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Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.7 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a strong phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which beats the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.
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The prevailing metaphor used in the study of creative cognition is the metaphor of evolution (Gruber and Davis 1988). Naturally, AI experimenters have implemented algorithms based loosely or precisely on Darwinian models of information transfer and change over time in attempts to make machines more creative (Fogel and Owens 1966). Simulating an evolving systems model of creative expression raises the important technical issue of how to judge the fitness of the forms the system produces. The use of a neural network is an appropriate and fairly novel (see also Biles 1996 and Burton and Vladimirova, 1997) answer to the question of how a fuzzy and subjective measure can be captured and put to use. This technical paper proposes a plan for the design of a creative computational system of this kind, and documents a working prototype called Poevolve that embodies some of these features.
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The present paper presents an application that composes formal poetry in Spanish in a semiautomatic interactive fashion. Automatic Spanish Poetry Expert and Rewriting Application (ASPERA) is a forward reasoning rule-based system that obtains from the user basic style parameters and an intended message; applies a knowledge-based pre-processor to select the most appropriate metric structure for the user's wishes; and, by intelligent adaptation of selected examples from a corpus of verses, carries out a prose-to-poetry translation of the given message. In the composition process, ASPERA combines natural language generation and CBR techniques to apply a set of construction heuristics obtained from formal literature on Spanish poetry. If the user validates the poem draft presented by the system, the resulting verses are analysed and incorporated into the system data files.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
  • Bengio
Bengio. 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In Proceedings of the 2014
Chinese song iambics generation
  • Qixin Wang
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Qixin Wang, Tianyi Luo, Dong Wang, and Chao Xing. 2015. Chinese song iambics generation
Neural machine translation by jointly learning to align and translate
  • Yoshua Bengio
Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the 2015 International Conference on Learning Representations, San Diego, CA.