Abstract
Automatic paraphrase generation is an important task for natural language processing. However, progress in paraphrase generation has been hindered for a long time by the lack of large monolingual parallel corpora. We can alleviate the data shortage by effectively using multi-domain corpus. In this paper, we propose a novel model to exploit information from other source domains (out-of-domains) which benefits our target domain (in-domain). In our method, we maintain a private encoder and a private decoder for each domain which are used to model domain-specific information. In the meantime, we introduce a shared encoder and a shared decoder shared by all domains which only contain domain-independent information. Besides, we add a domain discriminator to the shared encoder to reinforce the ability to capture common features of shared encoder by adversarial training. Experimental results show that our method not only perform well in traditional domain adaptation tasks but also improve performance in all domains together. Moreover, we show that the shared layer learned by our proposed model can be regarded as an off-the-shelf layer and can be easily adapted to new domains.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Britz, D., Le, Q., Pryzant, R.: Effective domain mixing for neural machine translation. In: Proceedings of the Second Conference on Machine Translation, pp. 118–126. Association for Computational Linguistics, Copenhagen, September 2017. https://doi.org/10.18653/v1/W17-4712. https://www.aclweb.org/anthology/W17-4712
Fader, A., Zettlemoyer, L., Etzioni, O.: Paraphrase-driven learning for open question answering, vol. 1, pp. 1608–1618 (2013)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096-2030 (2015)
Gupta, A., Agarwal, A., Singh, P., Rai, P.: A deep generative framework for paraphrase generation (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Huang, S., Yu, W., Wei, F., Ming, Z.: Dictionary-guided editing networks for paraphrase generation (2018)
Lavie, A., Agarwal, A.: METEOR: an automatic metric for MT evaluation with high levels of correlation with human judgments (2007)
Madnani, N., Tetreault, J., Chodorow, M.: Re-examining machine translation metrics for paraphrase identification. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2012)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of Meeting of the Association for Computational Linguistics (2002)
Prakash, A., et al.: Neural paraphrase generation with stacked residual LSTM networks (2016)
Roy, A., Grangier, D.: Unsupervised paraphrasing without translation (2019)
Sutskever, I., Martens, J., Hinton, G.E.: Generating text with recurrent neural networks. In: International Conference on Machine Learning (2016)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks (2014)
Wang, R., Finch, A., Utiyama, M., Sumita, E.: Sentence embedding for neural machine translation domain adaptation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 560–566. Association for Computational Linguistics, Vancouver, July 2017. https://doi.org/10.18653/v1/P17-2089. https://www.aclweb.org/anthology/P17-2089
Wang, R., Utiyama, M., Liu, L., Chen, K., Sumita, E.: Instance weighting for neural machine translation domain adaptation. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1482–1488. Association for Computational Linguistics, Copenhagen, September 2017. https://doi.org/10.18653/v1/D17-1155. https://www.aclweb.org/anthology/D17-1155
Wubben, S., Van Den Bosch, A., Krahmer, E.: Paraphrase generation as monolingual translation: data and evaluation. In: International Natural Language Generation Conference (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Qiao, L., Li, Y., Zhong, C. (2021). Neural Paraphrase Generation with Multi-domain Corpus. 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 12891. Springer, Cham. https://doi.org/10.1007/978-3-030-86362-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-86362-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86361-6
Online ISBN: 978-3-030-86362-3
eBook Packages: Computer ScienceComputer Science (R0)