Paraphrase Generation for Semi-Supervised Learning in NLU

Eunah Cho, He Xie, William M. Campbell

Abstract
Semi-supervised learning is an efficient way to improve performance for natural language processing systems. In this work, we propose Para-SSL, a scheme to generate candidate utterances using paraphrasing and methods from semi-supervised learning. In order to perform paraphrase generation in the context of a dialog system, we automatically extract paraphrase pairs to create a paraphrase corpus. Using this data, we build a paraphrase generation system and perform one-to-many generation, followed by a validation step to select only the utterances with good quality. The paraphrase-based semi-supervised learning is applied to five functionalities in a natural language understanding system. Our proposed method for semi-supervised learning using paraphrase generation does not require user utterances and can be applied prior to releasing a new functionality to a system. Experiments show that we can achieve up to 19% of relative slot error reduction without an access to user utterances, and up to 35% when leveraging live traffic utterances.
Anthology ID:
W19-2306
Volume:
Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Antoine Bosselut, Asli Celikyilmaz, Marjan Ghazvininejad, Srinivasan Iyer, Urvashi Khandelwal, Hannah Rashkin, Thomas Wolf
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
45–54
Language:
URL:
https://aclanthology.org/W19-2306
DOI:
10.18653/v1/W19-2306
Bibkey:
Cite (ACL):
Eunah Cho, He Xie, and William M. Campbell. 2019. Paraphrase Generation for Semi-Supervised Learning in NLU. In Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation, pages 45–54, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Paraphrase Generation for Semi-Supervised Learning in NLU (Cho et al., NAACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/W19-2306.pdf
PDF Search