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
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
257–261
Language:
URL:
https://www.aclweb.org/anthology/W17-3542
DOI:
10.18653/v1/W17-3542
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-3542.pdf