Asking Clarifying Questions in Open-Domain Information-Seeking Conversations
Table 2. Performance of question retrieval model. The superscript * denotes statistically significant differences compared to all the baselines (p<0.001).
['[BOLD] Method', 'MAP', 'Recall@10', 'Recall@20', 'Recall@30']
Question retrieval. As we see, BERT-LeaQuR is able to outperform all baselines. It is worth noting that the model’s performance gets better as the number of retrieved documents increases. This indicates that BERT-LeaQuR is able to capture the relevance of query and questions when they lack common terms. In fact, we see that all term-matching retrieval models such as BM25 are significantly outperformed in terms of all evaluation metrics.