Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues

Jiao Ou, Jinchao Zhang, Yang Feng, Jie Zhou


Abstract
The construction of open-domain dialogue systems requires high-quality dialogue datasets. The dialogue data admits a wide variety of responses for a given dialogue history, especially responses with different semantics. However, collecting high-quality such a dataset in most scenarios is labor-intensive and time-consuming. In this paper, we propose a data augmentation method to automatically augment high-quality responses with different semantics by counterfactual inference. Specifically, given an observed dialogue, our counterfactual generation model first infers semantically different responses by replacing the observed reply perspective with substituted ones. Furthermore, our data selection method filters out detrimental augmented responses. Experimental results show that our data augmentation method can augment high-quality responses with different semantics for a given dialogue history, and can outperform competitive baselines on multiple downstream tasks.
Anthology ID:
2022.emnlp-main.106
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1635–1648
Language:
URL:
https://aclanthology.org/2022.emnlp-main.106
DOI:
Bibkey:
Cite (ACL):
Jiao Ou, Jinchao Zhang, Yang Feng, and Jie Zhou. 2022. Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1635–1648, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues (Ou et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.106.pdf