Towards Persona-Based Empathetic Conversational Models
Table 5: Test performance (in %) of CoBERT and all baselines. Values in bold denote best results.
['[BOLD] Models', '[BOLD] happy R@1', '[BOLD] happy R@10', '[BOLD] happy R@50', '[BOLD] happy MRR', '[BOLD] offmychest R@1', '[BOLD] offmychest R@10', '[BOLD] offmychest R@50', '[BOLD] offmychest MRR', '[BOLD] PEC (happy + offmychest) R@1', '[BOLD] PEC (happy + offmychest) R@10', '[BOLD] PEC (happy + offmychest) R@50', '[BOLD] PEC (happy + offmychest) MRR']
Among the non-pretrained models, DIM outperforms BoW and HLSTM by large margins on all datasets, demonstrating the importance of finer-grained matching and hierarchical aggregation for response selection. The simple Bi-encoder performs noticeably better than DIM, suggesting that sentence representation is another critical factor in response selection and that BERT can provide much richer representation than the BiLSTM used in DIM. Poly-encoder performs best among all baselines because it leverages the strengths of both BERT and attention-based finer-grained matching.