Ivan P. Yamshchikov


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Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites
Alexey Tikhonov | Viacheslav Shibaev | Aleksander Nagaev | Aigul Nugmanova | Ivan P. Yamshchikov
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

This paper shows that standard assessment methodology for style transfer has several significant problems. First, the standard metrics for style accuracy and semantics preservation vary significantly on different re-runs. Therefore one has to report error margins for the obtained results. Second, starting with certain values of bilingual evaluation understudy (BLEU) between input and output and accuracy of the sentiment transfer the optimization of these two standard metrics diverge from the intuitive goal of the style transfer task. Finally, due to the nature of the task itself, there is a specific dependence between these two metrics that could be easily manipulated. Under these circumstances, we suggest taking BLEU between input and human-written reformulations into consideration for benchmarks. We also propose three new architectures that outperform state of the art in terms of this metric.

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Decomposing Textual Information For Style Transfer
Ivan P. Yamshchikov | Viacheslav Shibaev | Aleksander Nagaev | Jürgen Jost | Alexey Tikhonov
Proceedings of the 3rd Workshop on Neural Generation and Translation

This paper focuses on latent representations that could effectively decompose different aspects of textual information. Using a framework of style transfer for texts, we propose several empirical methods to assess information decomposition quality. We validate these methods with several state-of-the-art textual style transfer methods. Higher quality of information decomposition corresponds to higher performance in terms of bilingual evaluation understudy (BLEU) between output and human-written reformulations.

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Dyr Bul Shchyl. Proxying Sound Symbolism With Word Embeddings
Ivan P. Yamshchikov | Viascheslav Shibaev | Alexey Tikhonov
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP

This paper explores modern word embeddings in the context of sound symbolism. Using basic properties of the representations space one can construct semantic axes. A method is proposed to measure if the presence of individual sounds in a given word shifts its semantics of that word along a specific axis. It is shown that, in accordance with several experimental and statistical results, word embeddings capture symbolism for certain sounds.


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Sounds Wilde. Phonetically Extended Embeddings for Author-Stylized Poetry Generation
Aleksey Tikhonov | Ivan P. Yamshchikov
Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

This paper addresses author-stylized text generation. Using a version of a language model with extended phonetic and semantic embeddings for poetry generation we show that phonetics has comparable contribution to the overall model performance as the information on the target author. Phonetic information is shown to be important for English and Russian language. Humans tend to attribute machine generated texts to the target author.