Unsupervised Context Rewriting for Open Domain Conversation
Table 4: Evaluation results on multi-turn response selection. The numbers of baselines are copied from Zhou et al. (2018)
['[EMPTY]', 'MAP', 'MRR', 'P@1', 'R10@1', 'R10@2', 'R10@5']
We can see that our model achieves a comparable performance with state-of-the-art DAM model, but only consuming a rewritten utterance rather than the whole context. This indicates that our model is able to recognize important content in context and generate a self-contained sentence. This argument is also verified by 1 point promotion compared with DAMsingle which only uses the last utterance as an input. Additionally, DAMsingle just underperforms DAM 1 point, meaning that the last utterance is very important for response selection. It supports our assumption that the last utterance is important which is a good prototype for context rewriting.