Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection

• URL: http://arxiv.org/abs/2208.09601v1
• Date: Sat, 20 Aug 2022 04:13:27 GMT
• Title: Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection
• Authors: Souvik Das, Sougata Saha, Rohini K. Srihari
• Abstract summary: We propose a suite of fusion strategies that capture the interaction between persona, emotion, and entailment information of utterances. We 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.
• Score: 2.8360662552057323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalized response selection systems are generally grounded on persona. However, there exists a co-relation between persona and empathy, which is not explored well in these systems. Also, faithfulness to the conversation context plunges when a contradictory or an off-topic response is selected. 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.

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