Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
TABLE V: Comparisons between U2R-IMN and U2U-IMN models on several subsets of the test set of the Ubuntu Dialogue Corpus V2. U2R-IMN denotes the model with concatenation of the utterances in a response. In each subset, the correct responses are composed of 1, 2, or 3 utterances.
['Model', 'Subset', '[BOLD] R2@1', '[BOLD] R10@1', '[BOLD] R10@2', '[BOLD] R10@5']
To further verify the effectiveness of our proposed U2U matching framework, we split the test set of the Ubuntu Dialogue Corpus V2 dataset according to the number of utterances in their correct responses. Then, the performances on these subsets of the U2U-IMN model were compared with those of the model (denoted U2R-IMN) As demonstrated, the U2U framework can help improve the performance by exploiting the relationships among the utterances in a response. We can see that the advantage of the U2U-IMN model over the U2R-IMN model became larger when the correct responses were composed of more utterances. This was consistent with the motivation of the U2U matching framework. Considering that only 30% of responses in the Ubuntu Dialogue Corpus V2 dataset consisted of multiple utterances, a larger overall improvement may be achieved when applying our proposed U2U models to datasets containing more responses with multiple utterances.