Data Augmentation for Neural Online Chats Response Selection

Wenchao Du, Alan Black


Abstract
Data augmentation seeks to manipulate the available data for training to improve the generalization ability of models. We investigate two data augmentation proxies, permutation and flipping, for neural dialog response selection task on various models over multiple datasets, including both Chinese and English languages. Different from standard data augmentation techniques, our method combines the original and synthesized data for prediction. Empirical results show that our approach can gain 1 to 3 recall-at-1 points over baseline models in both full-scale and small-scale settings.
Anthology ID:
W18-5708
Volume:
Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
52–58
Language:
URL:
https://www.aclweb.org/anthology/W18-5708
DOI:
10.18653/v1/W18-5708
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5708.pdf