TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots
Table 1: Experimental results on two public dialogue datasets. The table is segmented into three sections: Non-Attention models, Attention-based models and our models. The italics denotes the previous best results, and the scores in bold express the new state-of-the-art result of single model without any pre-training layer.
['[EMPTY]', '[BOLD] Ubuntu Dialogue Corpus R2@1', '[BOLD] Ubuntu Dialogue Corpus R10@1', '[BOLD] Ubuntu Dialogue Corpus R10@2', '[BOLD] Ubuntu Dialogue Corpus R10@5', '[BOLD] Douban Conversation Corpus MAP', '[BOLD] Douban Conversation Corpus MRR', '[BOLD] Douban Conversation Corpus P@1', '[BOLD] Douban Conversation Corpus R10@1', '[BOLD] Douban Conversation Corpus R10@2', '[BOLD] Douban Conversation Corpus R10@5']
Our results are obviously better on the two datasets compared with recently attention-based model DAM, which exceeds 2.3% in R10@1 of Ubuntu and 2.6% in P@1 of Douban. Furthermore, our score is significantly exceeding in almost all metrics except the R10@5 in Douban when compared with DUA, which may be because the metric is not very stable as the test set in Douban is very small (1000).