Answering Complex Open-domain Questions Through Iterative Query Generation
Table 2: End-to-end QA performance of baselines and our GoldEn Retriever model on the HotpotQA fullwiki test set. Among systems that were not published at the time of submission of this paper, “BERT pip.” was submitted to the official HotpotQA leaderboard on May 15th (thus contemporaneous), while “Entity-centric BERT Pipeline” and “PR-Bert” were submitted after the paper submission deadline.
['[BOLD] System', '[BOLD] Answer EM', '[BOLD] Answer F1', '[BOLD] Sup Fact EM', '[BOLD] Sup Fact F1', '[BOLD] Joint EM', '[BOLD] Joint F1']
We compare the end-to-end performance of GoldEn Retriever against several QA systems on the HotpotQA dataset: (1) the baseline presented in Yang et al. , (2) CogQA Ding et al. However, the QA performance is handicapped because we do not make use of pretrained contextualization models (e.g., BERT) that these systems use. We expect a boost in QA performance from adopting these more powerful question answering models, especially ones that are tailored to perform few-document multi-hop reasoning. We leave this to future work.