Long-term Control for Dialogue Generation: Methods and Evaluation
Ramya Ramakrishnan, Hashan Narangodage, Mauro Schilman, Kilian Weinberger, Ryan McDonald
Abstract
Current approaches for controlling dialogue response generation are primarily focused on high-level attributes like style, sentiment, or topic. In this work, we focus on constrained long-term dialogue generation, which involves more fine-grained control and requires a given set of control words to appear in generated responses. This setting requires a model to not only consider the generation of these control words in the immediate context, but also produce utterances that will encourage the generation of the words at some time in the (possibly distant) future. We define the problem of constrained long-term control for dialogue generation, identify gaps in current methods for evaluation, and propose new metrics that better measure long-term control. We also propose a retrieval-augmented method that improves performance of long-term controlled generation via logit modification techniques. We show through experiments on three task-oriented dialogue datasets that our metrics better assess dialogue control relative to current alternatives and that our method outperforms state-of-the-art constrained generation baselines.- Anthology ID:
- 2022.naacl-main.54
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 738–753
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.54
- DOI:
- 10.18653/v1/2022.naacl-main.54
- Cite (ACL):
- Ramya Ramakrishnan, Hashan Narangodage, Mauro Schilman, Kilian Weinberger, and Ryan McDonald. 2022. Long-term Control for Dialogue Generation: Methods and Evaluation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 738–753, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Long-term Control for Dialogue Generation: Methods and Evaluation (Ramakrishnan et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.naacl-main.54.pdf
- Code
- asappresearch/constrained-dialogue-generation