Advertisement

Aligning Sentences Between Comparable Texts of Different Styles

  • Xiwen ChenEmail author
  • Mengxue Zhang
  • Kenny Qili Zhu
Conference paper
  • 60 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1157)

Abstract

Monolingual parallel corpus is crucial for training and evaluating text rewriting or paraphrasing models. Aligning parallel sentences between two large body of texts is a key step toward automatic construction of such parallel corpora. We propose a greedy alignment algorithm that makes use of strong unsupervised similarity measures. The algorithm aligns sentences with state-of-the-art accuracy while being more robust on corpora with special linguistic features. Using this alignment algorithm, we automatically constructed a large English parallel corpus from various translated works of classic literature.

Keywords

Monolingual parallel corpora Sentence alignment Unsupervised algorithms 

References

  1. 1.
    Cer, D., et al.: Universal sentence encoder. arXiv preprint arXiv:1803.11175 (2018)
  2. 2.
    Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data. arXiv preprint arXiv:1705.02364 (2017)
  3. 3.
    Coster, W., Kauchak, D.: Learning to simplify sentences using Wikipedia. In: Proceedings of the Workshop on Monolingual Text-to-Text Generation, pp. 1–9. Association for Computational Linguistics (2011)Google Scholar
  4. 4.
    Hatzlvassiloglou, V., Klavans, J.L., Eskin, E.: Detecting text similarity over short passages: exploring linguistic feature combinations via machine learning. In: 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (1999)Google Scholar
  5. 5.
    Hwang, W., Hajishirzi, H., Ostendorf, M., Wu, W.: Aligning sentences from standard Wikipedia to simple Wikipedia. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 211–217 (2015)Google Scholar
  6. 6.
    Ji, Y., Eisenstein, J.: Discriminative improvements to distributional sentence similarity. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 891–896 (2013)Google Scholar
  7. 7.
    Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 427–431. Association for Computational Linguistics (April 2017)Google Scholar
  8. 8.
    Kajiwara, T., Komachi, M.: Building a monolingual parallel corpus for text simplification using sentence similarity based on alignment between word embeddings. In: Proceedings of COLING 2016, The 26th International Conference on Computational Linguistics: Technical Papers, pp. 1147–1158 (2016)Google Scholar
  9. 9.
    Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. Text Summarization Branches Out (2004)Google Scholar
  10. 10.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  11. 11.
    Mueller, J., Thyagarajan, A.: Siamese recurrent architectures for learning sentence similarity. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)Google Scholar
  12. 12.
    Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics (2002)Google Scholar
  13. 13.
    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
  14. 14.
    Zamani, H., Faili, H., Shakery, A.: Sentence alignment using local and global information. Comput. Speech Lang. 39, 88–107 (2016)CrossRefGoogle Scholar
  15. 15.
    Zhu, Z., Bernhard, D., Gurevych, I.: A monolingual tree-based translation model for sentence simplification. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 1353–1361. Association for Computational Linguistics (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Advanced Data and Programming Technology Lab, Computer Science DepartmentShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations