Neural Paraphrase Generation using Transfer Learning

Florin Brad, Traian Rebedea


Abstract
Progress in statistical paraphrase generation has been hindered for a long time by the lack of large monolingual parallel corpora. In this paper, we adapt the neural machine translation approach to paraphrase generation and perform transfer learning from the closely related task of entailment generation. We evaluate the model on the Microsoft Research Paraphrase (MSRP) corpus and show that the model is able to generate sentences that capture part of the original meaning, but fails to pick up on important words or to show large lexical variation.
Anthology ID:
W17-3542
Volume:
Proceedings of the 10th International Conference on Natural Language Generation
Month:
September
Year:
2017
Address:
Santiago de Compostela, Spain
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
257–261
Language:
URL:
https://aclanthology.org/W17-3542
DOI:
10.18653/v1/W17-3542
Bibkey:
Cite (ACL):
Florin Brad and Traian Rebedea. 2017. Neural Paraphrase Generation using Transfer Learning. In Proceedings of the 10th International Conference on Natural Language Generation, pages 257–261, Santiago de Compostela, Spain. Association for Computational Linguistics.
Cite (Informal):
Neural Paraphrase Generation using Transfer Learning (Brad & Rebedea, INLG 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/W17-3542.pdf
Data
SNLI