Name
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 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.18653/v1/2022.acl-long.221 | PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS) |
DocType | Volume | Citations |
Conference | Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siqian Shen | 1 | 0 | 0.68 |
| Verónica Pérez-Rosas | 2 | 40 | 5.02 |
| Charles Welch | 3 | 3 | 4.14 |
| Soujanya Poria | 4 | 1336 | 60.98 |
| Rada Mihalcea | 5 | 6460 | 445.54 |