MoEL: Mixture of Empathetic Listeners
Table 2: Comparison between our proposed methods and baselines. All of models receive close BLEU score. MoEL achieve highest Empathy and Relevance score, while TRS achieve better Fluency score. The number of parameters for each model is reported.
['[EMPTY]', '[BOLD] Params.', '[BOLD] BLEU', '[BOLD] Empathy', '[BOLD] Relevance', '[BOLD] Fluency']
TRS achieves the highest BLEU score and Fluency score but the lowest Empathy and Relevance score. This shows us that the responses generated by TRS are more generic but cannot accurately capture the user emotions. With the additional supervision on user emotions, multi-task training improves both Empathy and Relevance score, but it still degrades Fluency. In contrast, MoEL achieves the highest Empathy and Relevance score. This suggests that the multi-expert strategy helps to capture the user emotional states and context simultaneously, and elicits a more appropriate response. The human A/B tests also confirm that the responses from our model are more preferred by human judges.