Abstract
In this paper, we focus on the problem of text style transfer which is considered as a subtask of paraphrasing. Most previous paraphrasing studies have focused on the replacements of words and phrases, which depend exclusively on the availability of parallel or pseudo-parallel corpora. However, existing methods can not transfer the style of text completely or be independent from pair-wise corpora. This paper presents a novel sequence-to-sequence (Seq2Seq) based deep neural network model, using two switches with tensor product to control the style transfer in the encoding and decoding processes. Since massive parallel corpora are usually unavailable, the switches enable the model to conduct unsupervised learning, which is an initial investigation into the task of text style transfer to the best of our knowledge. The results are analyzed quantitatively and qualitatively, showing that the model can deal with paraphrasing at different text style transfer levels.
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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Barzilay, R.: Information fusion for multidocument summarization: paraphrasing and generation. Ph.D. thesis, Columbia University (2003)
Beltagy, I., Roller, S., Boleda, G., Erk, K., Mooney, R.J.: Utexas: natural language semantics using distributional semantics and probabilistic logic. In: SemEval 2014, p. 796 (2014)
Bjerva, J., Bos, J., Van der Goot, R., Nissim, M.: The meaning factory: formal semantics for recognizing textual entailment and determining semantic similarity. In: Proceedings of SemEval (2014)
Brown, P.F., Cocke, J., Pietra, S.A.D., Pietra, V.J.D., Jelinek, F., Lafferty, J.D., Mercer, R.L., Roossin, P.S.: A statistical approach to machine translation. Comput. Linguist. 16(2), 79–85 (1990)
Cao, Z., Luo, C., Li, W., Li, S.: Joint copying and restricted generation for paraphrase. arXiv preprint arXiv:1611.09235 (2016)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Ganitkevitch, J., Callison-Burch, C., Napoles, C., Van Durme, B.: Learning sentential paraphrases from bilingual parallel corpora for text-to-text generation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1168–1179. Association for Computational Linguistics (2011)
Ganitkevitch, J., Van Durme, B., Callison-Burch, C.: PPDB: the paraphrase database. In: HLT-NAACL, pp. 758–764 (2013)
Gatys, L.A., Ecker, A.S., Bethge, M.: Image transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)
Han, L., Kashyap, A., Finin, T., Mayfield, J., Weese, J.: UMBC EBIQUITY-CORE: semantic textual similarity systems. In: Proceedings of the Second Joint Conference on Lexical and Computational Semantics. vol. 1, pp. 44–52 (2013)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hu, B., Chen, Q., Zhu, F.: LCSTS: a large scale Chinese short text summarization dataset. arXiv preprint arXiv:1506.05865 (2015)
Ji, Y., Eisenstein, J.: Discriminative improvements to distributional sentence similarity. In: EMNLP, pp. 891–896 (2013)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kiros, R., Zhu, Y., Salakhutdinov, R.R., Zemel, R., Urtasun, R., Torralba, A., Fidler, S.: Skip-thought vectors. In: Advances in Neural Information Processing Systems, pp. 3294–3302 (2015)
Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., et al.: Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pp. 177–180. Association for Computational Linguistics (2007)
Li, X., Wu, X.: Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4520–4524. IEEE (2015)
Liu, G., Rosello, P., Sebastian, E.: Style transfer with non-parallel corpora. http://prosello.com/papers/style-transfer-s16.pdf
Madnani, N., Dorr, B.J.: Generating phrasal and sentential paraphrases: a survey of data-driven methods. Comput. Linguist. 36(3), 341–387 (2010)
Mikolov, T., Karafiát, M., Burget, L., Cernockỳ, J., Khudanpur, S.: Recurrent neural network based language model. In: Interspeech, vol. 2, p. 3 (2010)
Post, M., Ganitkevitch, J., Orland, L., Weese, J., Cao, Y., Callison-Burch, C., Irvine, A., Zaidan, O.F., et al.: Semi-Markov phrase-based monolingual alignment. In: Proceedings of EMNLP, vol. 1, pp. 166–177. Association for Computational Linguistics (2013)
Prakash, A., Hasan, S.A., Lee, K., Datla, V., Qadir, A., Liu, J., Farri, O.: Neural paraphrase generation with stacked residual LSTM networks. arXiv preprint arXiv:1610.03098 (2016)
Rastogi, P., Van Durme, B., Arora, R.: Multiview LSA: representation learning via generalized CCA. In: HLT-NAACL, pp. 556–566 (2015)
Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685 (2015)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)
Serban, I.V., Klinger, T., Tesauro, G., Talamadupula, K., Zhou, B., Bengio, Y., Courville, A.: Multiresolution recurrent neural networks: an application to dialogue response generation. arXiv preprint arXiv:1606.00776 (2016)
Socher, R., Huang, E.H., Pennington, J., Ng, A.Y., Manning, C.D.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: NIPS, vol. 24, pp. 801–809 (2011)
Sultan, M.A., Bethard, S., Sumner, T.: DLS@CU: sentence similarity from word alignment. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 241–246 (2014)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems. pp. 3104–3112 (2014)
Xu, W., Ritter, A., Dolan, W.B., Grishman, R., Cherry, C.: Paraphrasing for style. In: 24th International Conference on Computational Linguistics, COLING 2012 (2012)
Yin, W., Schütze, H.: Convolutional neural network for paraphrase identification. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 901–911 (2015)
Yu, M., Dredze, M.: Improving lexical embeddings with semantic knowledge. In: ACL (2), pp. 545–550 (2014)
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Han, M., Wu, O., Niu, Z. (2018). Unsupervised Automatic Text Style Transfer Using LSTM. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_24
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