Papers › Answering Open-Domain Questions of Varying Reasoning Steps from Text

Answering Open-Domain Questions of Varying Reasoning Steps from Text

23 Oct 2020EMNLP 2021 11arXiv:2010.12527archive 2025-07-28

Peng Qi, Haejun Lee, Oghenetegiri "TG" Sido, Christopher D. Manning

We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps. We employ a single multi-task transformer model to perform all the necessary subtasks -- retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents -- in an iterative fashion. We avoid crucial assumptions of previous work that do not transfer well to real-world settings, including exploiting knowledge of the fixed number of retrieval steps required to answer each question or using structured metadata like knowledge bases or web links that have limited availability. Instead, we design a system that can answer open-domain questions on any text collection without prior knowledge of reasoning complexity. To emulate this setting, we construct a new benchmark, called BeerQA, by combining existing one- and two-step datasets with a new collection of 530 questions that require three Wikipedia pages to answer, unifying Wikipedia corpora versions in the process. We show that our model demonstrates competitive performance on both existing benchmarks and this new benchmark. We make the new benchmark available at https://beerqa.github.io/.

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Code

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beerqa/irrr officialmentioned in papermentioned on GitHubtfNOASSERTION report

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Code Syntology ran Syntology

3 samples harvested; 3 ran; 1 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
1ran · violated contract
1ran · our draft was wrong

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exact_match_score identical code first harvested elsewhere ran · violated contract fingerprinted licence of this copy not recorded · f6c275d6a18330a9 · report
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normalize_answer identical code first harvested elsewhere ran · our draft was wrong fingerprinted licence of this copy not recorded · c6a80c065d2e4851 · report

Tasks

Open-Domain Question AnsweringQuestion AnsweringRerankingRetrieval

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering HotpotQA IRRR+ ANS-EM 0.663 #9 of 72 Archive leaderboard report
Question Answering HotpotQA IRRR+ ANS-F1 0.791 #9 of 72 Archive leaderboard report
Question Answering HotpotQA IRRR+ JOINT-EM 0.428 #9 of 72 Archive leaderboard report
Question Answering HotpotQA IRRR+ JOINT-F1 0.696 #9 of 72 Archive leaderboard report
Question Answering HotpotQA IRRR+ SUP-EM 0.569 #9 of 72 Archive leaderboard report
Question Answering HotpotQA IRRR+ SUP-F1 0.832 #9 of 72 Archive leaderboard report
Question Answering HotpotQA IRRR ANS-EM 0.657 #11 of 72 Archive leaderboard report
Question Answering HotpotQA IRRR ANS-F1 0.782 #11 of 72 Archive leaderboard report
Question Answering HotpotQA IRRR JOINT-EM 0.421 #11 of 72 Archive leaderboard report
Question Answering HotpotQA IRRR JOINT-F1 0.686 #11 of 72 Archive leaderboard report
Question Answering HotpotQA IRRR SUP-EM 0.559 #11 of 72 Archive leaderboard report
Question Answering HotpotQA IRRR SUP-F1 0.821 #11 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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