Simple and Effective Paraphrastic Similarity from Parallel Translations
John Wieting, Kevin Gimpel, Graham Neubig, Taylor Berg-Kirkpatrick
Abstract
We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating para-phrase corpora. Further, we show that the resulting model can be applied to cross lingual tasks where it both outperforms and is orders of magnitude faster than more complex state-of-the-art baselines.- Anthology ID:
- P19-1453
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4602–4608
- Language:
- URL:
- https://aclanthology.org/P19-1453
- DOI:
- 10.18653/v1/P19-1453
- Cite (ACL):
- John Wieting, Kevin Gimpel, Graham Neubig, and Taylor Berg-Kirkpatrick. 2019. Simple and Effective Paraphrastic Similarity from Parallel Translations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4602–4608, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Simple and Effective Paraphrastic Similarity from Parallel Translations (Wieting et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/P19-1453.pdf
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