Abstract
Recognizing and generating paraphrases is an important component in many natural language processing applications. A well-established technique for automatically extracting paraphrases leverages bilingual corpora to find meaning-equivalent phrases in a single language by “pivoting” over a shared translation in another language. In this paper we revisit bilingual pivoting in the context of neural machine translation and present a paraphrasing model based purely on neural networks. Our model represents paraphrases in a continuous space, estimates the degree of semantic relatedness between text segments of arbitrary length, and generates candidate paraphrases for any source input. Experimental results across tasks and datasets show that neural paraphrases outperform those obtained with conventional phrase-based pivoting approaches.- Anthology ID:
- E17-1083
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 881–893
- Language:
- URL:
- https://aclanthology.org/E17-1083
- DOI:
- Cite (ACL):
- Jonathan Mallinson, Rico Sennrich, and Mirella Lapata. 2017. Paraphrasing Revisited with Neural Machine Translation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 881–893, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Paraphrasing Revisited with Neural Machine Translation (Mallinson et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/E17-1083.pdf