Generating Syntactic Paraphrases

Emilie Colin, Claire Gardent


Abstract
We study the automatic generation of syntactic paraphrases using four different models for generation: data-to-text generation, text-to-text generation, text reduction and text expansion, We derive training data for each of these tasks from the WebNLG dataset and we show (i) that conditioning generation on syntactic constraints effectively permits the generation of syntactically distinct paraphrases for the same input and (ii) that exploiting different types of input (data, text or data+text) further increases the number of distinct paraphrases that can be generated for a given input.
Anthology ID:
D18-1113
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
937–943
Language:
URL:
https://aclanthology.org/D18-1113
DOI:
10.18653/v1/D18-1113
Bibkey:
Cite (ACL):
Emilie Colin and Claire Gardent. 2018. Generating Syntactic Paraphrases. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 937–943, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Generating Syntactic Paraphrases (Colin & Gardent, EMNLP 2018)
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PDF:
https://aclanthology.org/D18-1113.pdf
Video:
 https://aclanthology.org/D18-1113.mp4