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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P19-1453.pdf
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