Papers › Making Neural QA as Simple as Possible but not Simpler

Making Neural QA as Simple as Possible but not Simpler

14 Mar 2017CONLL 2017 8arXiv:1703.04816archive 2025-07-28

Dirk Weissenborn, Georg Wiese, Laura Seiffe

Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would justify their complexity. In this work, we propose a simple heuristic that guides the development of neural baseline systems for the extractive QA task. We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, a composition function that goes beyond simple bag-of-words modeling, such as recurrent neural networks. Our results show that FastQA, a system that meets these two requirements, can achieve very competitive performance compared with existing models. We argue that this surprising finding puts results of previous systems and the complexity of recent QA datasets into perspective.

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fetch_parents uclmr/jack/bin/jack-train.py community (archive-listed) unverified MIT (permissive) · 827e812c314c4426 · report
jack_to_qasetting uclmr/jack/jack/core/data_structures.py community (archive-listed) unverified MIT (permissive) · 4961fc4669cf2576 · report

Tasks

Question AnsweringReading Comprehension

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering NewsQA FastQAExt EM 43.7 #15 of 18 Archive leaderboard report
Question Answering NewsQA FastQAExt F1 56.1 #15 of 18 Archive leaderboard report
Question Answering SQuAD1.1 FastQAExt EM 70.849 #156 of 213 Archive leaderboard report
Question Answering SQuAD1.1 FastQAExt F1 78.857 #156 of 213 Archive leaderboard report
Question Answering SQuAD1.1 FastQA EM 68.436 #166 of 213 Archive leaderboard report
Question Answering SQuAD1.1 FastQA F1 77.070 #166 of 213 Archive leaderboard report
Question Answering SQuAD1.1 dev FastQAExt (beam-size 5) EM 70.3 #37 of 55 Archive leaderboard report
Question Answering SQuAD1.1 dev FastQAExt (beam-size 5) F1 78.5 #37 of 55 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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