You Impress Me: Dialogue Generation via Mutual Persona Perception
Table 5: Experimental results on Persona Perception.
['[BOLD] Model', '[BOLD] Original Hits@1\u2009↑', '[BOLD] Original MRR\u2009↑', '[BOLD] Revised Hits@1\u2009↑', '[BOLD] Revised MRR\u2009↑']
Receiver plays an important role in our approach, and we are interested in its capability on perceiving persona. Therefore, we conducted experiments on a synthesized dataset. We constructed the dataset by sampling 31 persona distractors for each dialogue in Persona-Chat. Two widely used ranking metrics were used to evaluate the performance: Hits@1 and Mean Reciprocal Rank (MRR). Given a dialogue and the complete set of profile sentences, MRR is the average reciprocal ranks of the dialogue-relevant profile sentences. Two simple baselines Random and IR Sordoni et al. As observed, our approach achieved excellent results on both original and revised modes. For example, compared with the IR baseline, our approach achieved an absolute improvement of 26.3% on Hits@1 in the original mode. In addition, the surprising results in the revised mode further demonstrate Receiver’s capability to perceive rephrased persona.