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Style as Sentiment Versus Style as Formality: The Same or Different?

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12895)

Abstract

Unsupervised textual style transfer presupposes that style is a coherent and consistent concept and that style transfer approaches will generalise consistently across different domains of style. This paper explores whether this presupposition is appropriate for different types of style. We explore this question by comparing the performance and latent representations of a variety of neural encoder-decoder style-transfer architecture when applied to sentiment transfer and formality transfer. Our findings indicate that the relationship between style and content shifts between these different domains of style: for sentiment, style and content are closely entangled; however, for formality, they are less entangled. Our findings suggest that for different types of styles different approaches to modeling style for style-transfer are necessary.

Keywords

  • Content
  • Style
  • Sentiment
  • Formality
  • Disentanglement

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Notes

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References

  1. Bahdanau, D., Cho, K.H., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations, ICLR 2015 (2015)

    Google Scholar 

  2. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning, December 2014 (2014)

    Google Scholar 

  3. Conneau, A., Kruszewski, G., Lample, G., Barrault, L., Baroni, M.: What you can cram into a single vector: Probing sentence embeddings for linguistic properties. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2126–2136 (2018)

    Google Scholar 

  4. Fu, Z., Tan, X., Peng, N., Zhao, D., Yan, R.: Style transfer in text: exploration and evaluation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  5. Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680. Curran Associates, Inc. (2014). http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf

  6. Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., Xing, E.P.: Controllable text generation. CoRR abs/1703.00955 (2017). http://arxiv.org/abs/1703.00955

  7. Jafaritazehjani, S., Lecorvé, G., Lolive, D., Kelleher, J.D.: Style versus content: a distinction without a (learnable) difference? In: Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Association for Computational Linguistics (2020)

    Google Scholar 

  8. John, V., Mou, L., Bahuleyan, H., Vechtomova, O.: Disentangled representation learning for non-parallel text style transfer. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 424–434 (2019)

    Google Scholar 

  9. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)

    Google Scholar 

  10. Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, PMLR, Lille, France, 07–09 Jul 2015, vol. 37, pp. 957–966 (2015). http://proceedings.mlr.press/v37/kusnerb15.html

  11. Leeftink, W., Spanakis, G.: Towards controlled transformation of sentiment in sentences. CoRR abs/1808.04365 (2019). http://arxiv.org/abs/1808.04365

  12. Li, J., Jia, R., He, H., Liang, P.: Delete, retrieve, generate: a simple approach to sentiment and style transfer. In: 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018, pp. 1865–1874. Association for Computational Linguistics (ACL) (2018)

    Google Scholar 

  13. Ma, S., Sun, X.: A semantic relevance based neural network for text summarization and text simplification. Comput. Linguist. 1(1) (2017)

    Google Scholar 

  14. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP (2014)

    Google Scholar 

  15. Prabhumoye, S., Tsvetkov, Y., Salakhutdinov, R., Black, A.W.: Style transfer through back-translation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 866–876. Association for Computational Linguistics (2018). http://aclweb.org/anthology/P18-1080

  16. Ramos, J., et al.: Using TF-IDF to determine word relevance in document queries. Citeseer

    Google Scholar 

  17. Rao, S., Tetreault, J.R.: Dear sir or madam, may i introduce the GYAFC dataset: Corpus, benchmarks and metrics for formality style transfer. In: NAACL-HLT (2018)

    Google Scholar 

  18. Romanov, A., Rumshisky, A., Rogers, A., Donahue, D.: Adversarial decomposition of text representation. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 815–825 (2019)

    Google Scholar 

  19. Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)

    CrossRef  Google Scholar 

  20. Shen, T., Lei, T., Barzilay, R., Jaakkola, T.: Style transfer from non-parallel text by cross-alignment. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 6830–6841. Curran Associates, Inc. (2017). http://papers.nips.cc/paper/7259-style-transfer-from-non-parallel-text-by-cross-alignment.pdf

  21. Shivakumar, P.G., Georgiou, P.: Confusion2Vec: towards enriching vector space word representations with representational ambiguities. PeerJ Comput. Sci. 5, e195 (2019)

    Google Scholar 

  22. Singh, A., Palod, R.: Sentiment transfer using seq2seq adversarial autoencoders. CoRR abs/1804.04003 (2018). http://arxiv.org/abs/1804.04003

  23. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of the Conference in Neural Information Processing Systems (NIPS), pp. 3104–3112 (2014)

    Google Scholar 

  24. Tikhonov, A., Yamshchikov, I.P.: What is wrong with style transfer for texts? CoRR abs/1808.04365 (2018). http://arxiv.org/abs/1808.04365

  25. Yamshchikov, I., Shibaev, V., Khlebnikov, N., Tikhonov, A.: Style-transfer and paraphrase: looking for a sensible semantic similarity metric. arXiv preprint arXiv:2004.05001 (2020)

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Correspondence to Somayeh Jafaritazehjani .

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Jafaritazehjani, S., Lecorvé, G., Lolive, D., Kelleher, J.D. (2021). Style as Sentiment Versus Style as Formality: The Same or Different?. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12895. Springer, Cham. https://doi.org/10.1007/978-3-030-86383-8_39

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  • DOI: https://doi.org/10.1007/978-3-030-86383-8_39

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