Answering Complex Open-domain Questions Through Iterative Query Generation
Table 3: Question answering and IR performance amongst different IR settings on the dev set. We observe that although improving the IR engine is helpful, most of the performance gain results from the iterative retrieve-and-read strategy of GoldEn Retriever. (*: for GoldEn Retriever, the 10 paragraphs are combined from both hops, 5 from each hop.)
['[BOLD] Setting', '[BOLD] Ans F1', '[BOLD] Sup F1', '[BOLD] R@10∗']
In all cases, we use the QA component in GoldEn Retriever for the final question answering step. Further inspection reveals that despite Elasticsearch improving overall recall of gold documents, it is only able to retrieve both gold documents for 36.91% of the dev set questions, in comparison to 28.21% from the IR engine in Yang et al. In contrast, GoldEn Retriever improves this percentage to 61.01%, almost doubling the recall over the single-hop baseline, providing the QA component a much better set of context documents to predict answers from.