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Personalized response selection systems are generally grounded on persona.However, there exists a co-relation between persona and empathy, which is notexplored well in these systems. Also, faithfulness to the conversation contextplunges when a contradictory or an off-topic response is selected. This paperattempts to address these issues by proposing a suite of fusion strategies thatcapture the interaction between persona, emotion, and entailment information ofthe utterances. Ablation studies on the Persona-Chat dataset show thatincorporating emotion and entailment improves the accuracy of responseselection. We combine our fusion strategies and concept-flow encoding to traina BERT-based model which outperforms the previous methods by margins largerthan 2.3 % on original personas and 1.9 % on revised personas in terms ofhits@1 (top-1 accuracy), achieving a new state-of-the-art performance on thePersona-Chat dataset.

Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection