Abstract
The natural language generation (NLG) module in a task-oriented dialogue system produces user-facing utterances conveying required information. Thus, it is critical for the generated response to be natural and fluent. We propose to integrate adversarial training to produce more human-like responses. The model uses Straight-Through Gumbel-Softmax estimator for gradient computation. We also propose a two-stage training scheme to boost performance. Empirical results show that the adversarial training can effectively improve the quality of language generation in both automatic and human evaluations. For example, in the RNN-LG Restaurant dataset, our model AdvNLG outperforms the previous state-of-the-art result by 3.6% in BLEU.- Anthology ID:
- 2020.sigdial-1.33
- Volume:
- Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- July
- Year:
- 2020
- Address:
- 1st virtual meeting
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 265–271
- Language:
- URL:
- https://aclanthology.org/2020.sigdial-1.33
- DOI:
- Cite (ACL):
- Chenguang Zhu. 2020. Boosting Naturalness of Language in Task-oriented Dialogues via Adversarial Training. In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 265–271, 1st virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- Boosting Naturalness of Language in Task-oriented Dialogues via Adversarial Training (Zhu, SIGDIAL 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.sigdial-1.33.pdf