Nothing Special   »   [go: up one dir, main page]

Skip to main content

Local or Global: The Variation in the Encoding of Style Across Sentiment and Formality

  • Conference paper
  • First Online:
Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14263))

Included in the following conference series:

  • 804 Accesses

Abstract

Research on textual style transfer has observed that the concept of style can vary across domains. This research examines the encoding of style across the sentiment and formality domains and observes that formality appears to be more globally encoded, and sentiment more locally encoded. The work also shows how the encoding of a style can inform the appropriate choice of method to compute content preservation during textual style transfer.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    The code is released: https://github.com/somayeJ/Transformer-based-style-transfer.

  2. 2.

    Other hyperparameters adapted from (http://github.com/fastnlp/style-transformer).

  3. 3.

    http://huggingface.co/cross-encoder/stsb-TinyBERT-L-4.

  4. 4.

    Training of our T-based model on Yelp took around 36 h using single Quadro RTX 8000 ss GPU, compared to 75 h while applying the training regime from [4].

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: Proceedings of the 28th Neural Information Processing Systems (NIPS), Workshop on Deep Learning (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 (ACL), vol. 1: Long Papers, pp. 2126–2136 (2018)

    Google Scholar 

  4. Dai, N., Liang, J., Qiu, X., Huang, X.: Style transformer: unpaired text style transfer without disentangled latent representation. CoRR abs/1905.05621 (2019). http://arxiv.org/abs/1905.05621

  5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  6. Elazar, Y., Goldberg, Y.: Adversarial removal of demographic attributes from text data. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 11–21. Association for Computational Linguistics, Brussels, Belgium (2018). https://doi.org/10.18653/v1/D18-1002. https://aclanthology.org/D18-1002

  7. 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 

  8. 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

  9. Jafaritazehjani, S., Lecorvé, G., Lolive, D., Kelleher, J.: Style versus content: a distinction without a (learnable) difference? In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 2169–2180. International Committee on Computational Linguistics, Barcelona, Spain (2020). https://doi.org/10.18653/v1/2020.coling-main.197, https://aclanthology.org/2020.coling-main.197

  10. Jafaritazehjani, S., Lecorvé, G., Lolive, D., Kelleher, J.D.: Style as sentiment versus style as formality: the same or different? In: ICANN (2021)

    Google Scholar 

  11. Jin, D., Jin, Z., Hu, Z., Vechtomova, O., Mihalcea, R.: Deep learning for text style transfer: a survey. Comput. Linguist. 48(1), 155–205 (2022). https://doi.org/10.1162/coli_a_00426

    Article  Google Scholar 

  12. 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 

  13. Kelleher, J.D.: Deep Learning. MIT Press, Cambridge (2019)

    Book  Google Scholar 

  14. 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 

  15. Lample, G., Subramanian, S., Smith, E., Denoyer, L., Ranzato, M., Boureau, Y.L.: Multiple-attribute text rewriting. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=H1g2NhC5KQ

  16. Leeftink, W., Spanakis, G.: Towards controlled transformation of sentiment in sentences. In: Proceedings of the 11th International Conference on Agents and Artificial Intelligence, vol. 2: ICAART, pp. 809–816. SCITEPRESS (2019)

    Google Scholar 

  17. Li, J., Jia, R., He, H., Liang, P.: Delete, retrieve, generate: a simple approach to sentiment and style transfer. In: Proceedings of the 16th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), vol. 1 (Long Papers), pp. 1865–1874 (2018)

    Google Scholar 

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

    Google Scholar 

  19. Nedumpozhimana, V., Kelleher, J.: Finding BERT’s idiomatic key. In: Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021), pp. 57–62. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.mwe-1.7, https://aclanthology.org/2021.mwe-1.7

  20. Nedumpozhimana, V., Klubička, F., Kelleher, J.D.: Shapley idioms: analysing BERT sentence embeddings for general idiom token identification. Front. Artif. Intell. 5, 813967 (2022). https://doi.org/10.3389/frai.2022.813967, https://www.frontiersin.org/article/10.3389/frai.2022.813967

  21. Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  22. 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 (ACL), Volume 1: Long Papers, pp. 866–876. Association for Computational Linguistics (2018). http://aclweb.org/anthology/P18-1080

  23. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018)

    Google Scholar 

  24. 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 

  25. Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2019). https://arxiv.org/abs/1908.10084

  26. 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 (NAACL-HLT), vol. 1 (Long and Short Papers), pp. 815–825 (2019)

    Google Scholar 

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

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

  29. 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 

  30. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  31. Zhao, J., Kim, Y., Zhang, K., Rush, A., LeCun, Y.: Adversarially regularized autoencoders. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 5902–5911. PMLR, Stockholm (2018). http://proceedings.mlr.press/v80/zhao18b.html

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Somayeh Jafaritazehjani .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jafaritazehjani, S., Lecorvé, G., Lolive, D., Kelleher, J.D. (2023). Local or Global: The Variation in the Encoding of Style Across Sentiment and Formality. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14263. Springer, Cham. https://doi.org/10.1007/978-3-031-44204-9_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-44204-9_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44203-2

  • Online ISBN: 978-3-031-44204-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics