A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots
Table 3: Evaluation results on the test sets of the PERSONA-CHAT data and the CMUDoG data. Numbers in bold mean that improvement over the best baseline is statistically significant (t-test, p-value <0.01).
['ModelsMetrics', 'PERSONA-CHAT Original Persona', 'PERSONA-CHAT Original Persona', 'PERSONA-CHAT Original Persona', 'PERSONA-CHAT Revised Persona', 'PERSONA-CHAT Revised Persona', 'PERSONA-CHAT Revised Persona', 'CMUDoG', 'CMUDoG', 'CMUDoG']
We can see that on both data sets, DGMN outperforms all baselines over all metrics, and the improvement is statistically significant (t-test, p-value <0.01). Improvement from DGMN over Transformer on the CMUDoG data is smaller than that on the PERSONA-CHAT data. The reason might be that Transformer can benefit from the wiki documents in CMUDoG that are longer and contain richer semantics than those handcrafted ones in PERSONA-CHAT.