  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.

Computation and Language;Artificial Intelligence