Abstract
Personalized response selection systems are generally grounded on persona. However, a correlation exists between persona and empathy, which these systems do not explore well. Also, when a contradictory or off-topic response is selected, faithfulness to the conversation context plunges. This paper attempts to address these issues by proposing a suite of fusion strategies that capture the interaction between persona, emotion, and entailment information of the utterances. Ablation studies on the Persona-Chat dataset show that incorporating emotion and entailment improves the accuracy of response selection. We combine our fusion strategies and concept-flow encoding to train a BERT-based model which outperforms the previous methods by margins larger than 2.3% on original personas and 1.9% on revised personas in terms of hits@1 (top-1 accuracy), achieving a new state-of-the-art performance on the Persona-Chat dataset- Anthology ID:
- 2022.coling-1.44
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 532–541
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.44
- DOI:
- Cite (ACL):
- Souvik Das, Sougata Saha, and Rohini K. Srihari. 2022. Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 532–541, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection (Das et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.coling-1.44.pdf