Knowledge Enhanced Reflection Generation for Counseling Dialogues

Siqi Shen, Veronica Perez-Rosas, Charles Welch, Soujanya Poria, Rada Mihalcea


Abstract
In this paper, we study the effect of commonsense and domain knowledge while generating responses in counseling conversations using retrieval and generative methods for knowledge integration. We propose a pipeline that collects domain knowledge through web mining, and show that retrieval from both domain-specific and commonsense knowledge bases improves the quality of generated responses. We also present a model that incorporates knowledge generated by COMET using soft positional encoding and masked self-attention.We show that both retrieved and COMET-generated knowledge improve the system’s performance as measured by automatic metrics and also by human evaluation. Lastly, we present a comparative study on the types of knowledge encoded by our system showing that causal and intentional relationships benefit the generation task more than other types of commonsense relations.
Anthology ID:
2022.acl-long.221
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3096–3107
Language:
URL:
https://aclanthology.org/2022.acl-long.221
DOI:
10.18653/v1/2022.acl-long.221
Bibkey:
Cite (ACL):
Siqi Shen, Veronica Perez-Rosas, Charles Welch, Soujanya Poria, and Rada Mihalcea. 2022. Knowledge Enhanced Reflection Generation for Counseling Dialogues. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3096–3107, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Knowledge Enhanced Reflection Generation for Counseling Dialogues (Shen et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.221.pdf
Data
ConceptNet