Abstract
We present a new unsupervised method for learning general-purpose sentence embeddings. Unlike existing methods which rely on local contexts, such as words inside the sentence or immediately neighboring sentences, our method selects, for each target sentence, influential sentences from the entire document based on the document structure. We identify a dependency structure of sentences using metadata and text styles. Additionally, we propose an out-of-vocabulary word handling technique for the neural network outputs to model many domain-specific terms which were mostly discarded by existing sentence embedding training methods. We empirically show that the model relies on the proposed dependencies more than the sequential dependency in many cases. We also validate our model on several NLP tasks showing 23% F1-score improvement in coreference resolution in a technical domain and 5% accuracy increase in paraphrase detection compared to baselines.
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Notes
- 1.
For simplicity, we use G to denote a \(S_t\) specific set.
References
Clark, K., Manning, C.D.: Deep reinforcement learning for mention-ranking coreference models. In: Empirical Methods on Natural Language Processing (EMNLP) (2016)
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data. In: The Conference on Empirical Methods on Natural Language Processing (EMNLP) (2017)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12(Jul), 2121–2159 (2011)
Gan, Z., Pu, Y., Henao, R., Li, C., He, X., Carin, L.: Learning generic sentence representations using convolutional neural networks. In: Proceedings of the 2017 Conference on Empirical Methods on Natural Language Processing (EMNLP) (2017)
Horn, F.: Context encoders as a simple but powerful extension of word2vec. In: Proceedings of the 2nd Workshop on Representation Learning for NLP, pp. 10–14 (2017)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 655–665 (2014)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, (EMNLP), pp. 1746–1751 (2014)
Kiros, R., et al.: Skip-thought vectors. In: Proceedings of the 29th Annual Conference on Neural Information Processing Systems (NIPS), pp. 3294–3302 (2015)
Kusner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q.: From word embeddings to document distances. In: Proceedings of the 32nd International Conference on Machine Learning (ICML), pp. 957–966 (2015)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31th International Conference on Machine Learning, (ICML), pp. 1188–1196 (2014)
Levy, O., Goldberg, Y.: Dependency-based word embeddings. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 302–308 (2014)
Luong, M.T., Sutskever, I., Le, Q.V., Vinyals, O., Zaremba, W.: Addressing the rare word problem in neural machine translation. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP), pp. 11–19 (2015)
Ma, M., Huang, L., Xiang, B., Zhou, B.: Dependency-based convolutional neural networks for sentence embedding. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 174–179. Association for Computational Linguistics, Beijing, July 2015
Microsoft: Microsoft research paraphrase corpus (2016). https://www.microsoft.com/en-us/download/details.aspx?id=52398
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 27th Annual Conference on Neural Information Processing Systems (NIPS), pp. 3111–3119 (2013)
Moosavi, N.S., Strube, M.: Which coreference evaluation metric do you trust? A proposal for a link-based entity aware metric. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 632–642 (2016)
Palangi, H., et al.: Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval. Proc. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 24(4), 694–707 (2016)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 2227–2237. Association for Computational Linguistics (2018). https://doi.org/10.18653/v1/N18-1202. http://aclweb.org/anthology/N18-1202
Santos, C.D., Zadrozny, B.: Learning character-level representations for part-of-speech tagging. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014), pp. 1818–1826 (2014)
Socher, R., Lin, C.C., Ng, A.Y., Manning, C.D.: Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 26th International Conference on Machine Learning (ICML) (2011)
Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 1556–1566 (2015)
Wieting, J., Gimpel, K.: Revisiting recurrent networks for paraphrastic sentence embeddings. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 2078–2088. Association for Computational Linguistics (2017)
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Lee, T., Park, Y. (2020). Unsupervised Sentence Embedding Using Document Structure-Based Context. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11907. Springer, Cham. https://doi.org/10.1007/978-3-030-46147-8_38
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