Papers › Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

24 Nov 2019ICLR 2020 1arXiv:1911.10470archive 2025-07-28

Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, Caiming Xiong

Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question. This paper introduces a new graph-based recurrent retrieval approach that learns to retrieve reasoning paths over the Wikipedia graph to answer multi-hop open-domain questions. Our retriever model trains a recurrent neural network that learns to sequentially retrieve evidence paragraphs in the reasoning path by conditioning on the previously retrieved documents. Our reader model ranks the reasoning paths and extracts the answer span included in the best reasoning path. Experimental results show state-of-the-art results in three open-domain QA datasets, showcasing the effectiveness and robustness of our method. Notably, our method achieves significant improvement in HotpotQA, outperforming the previous best model by more than 14 points.

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AkariAsai/learning_to_retrieve_reasoning_paths officialmentioned in papermentioned on GitHubpytorchMIT report
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exact_match_score AkariAsai/learning_to_retrieve_reasoning_paths/eval_utils.py official repository ran · violated contract fingerprinted MIT (permissive) · f6c275d6a18330a9 · report
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Tasks

Question AnsweringRetrieval

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering HotpotQA Robustly Fine-tuned Graph-based Recurrent Retriever ANS-EM 0.600 #26 of 72 Archive leaderboard report
Question Answering HotpotQA Robustly Fine-tuned Graph-based Recurrent Retriever ANS-F1 0.730 #26 of 72 Archive leaderboard report
Question Answering HotpotQA Robustly Fine-tuned Graph-based Recurrent Retriever JOINT-EM 0.354 #26 of 72 Archive leaderboard report
Question Answering HotpotQA Robustly Fine-tuned Graph-based Recurrent Retriever JOINT-F1 0.612 #26 of 72 Archive leaderboard report
Question Answering HotpotQA Robustly Fine-tuned Graph-based Recurrent Retriever SUP-EM 0.491 #26 of 72 Archive leaderboard report
Question Answering HotpotQA Robustly Fine-tuned Graph-based Recurrent Retriever SUP-F1 0.764 #26 of 72 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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