Controllable Paraphrase Generation with a Syntactic Exemplar

Mingda Chen, Qingming Tang, Sam Wiseman, Kevin Gimpel


Abstract
Prior work on controllable text generation usually assumes that the controlled attribute can take on one of a small set of values known a priori. In this work, we propose a novel task, where the syntax of a generated sentence is controlled rather by a sentential exemplar. To evaluate quantitatively with standard metrics, we create a novel dataset with human annotations. We also develop a variational model with a neural module specifically designed for capturing syntactic knowledge and several multitask training objectives to promote disentangled representation learning. Empirically, the proposed model is observed to achieve improvements over baselines and learn to capture desirable characteristics.
Anthology ID:
P19-1599
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:
5972–5984
Language:
URL:
https://www.aclweb.org/anthology/P19-1599
DOI:
10.18653/v1/P19-1599
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1599.pdf