Papers › A Question-Focused Multi-Factor Attention Network for Question Answering
A Question-Focused Multi-Factor Attention Network for Question Answering
Souvik Kundu, Hwee Tou Ng
Neural network models recently proposed for question answering (QA) primarily focus on capturing the passage-question relation. However, they have minimal capability to link relevant facts distributed across multiple sentences which is crucial in achieving deeper understanding, such as performing multi-sentence reasoning, co-reference resolution, etc. They also do not explicitly focus on the question and answer type which often plays a critical role in QA. In this paper, we propose a novel end-to-end question-focused multi-factor attention network for answer extraction. Multi-factor attentive encoding using tensor-based transformation aggregates meaningful facts even when they are located in multiple sentences. To implicitly infer the answer type, we also propose a max-attentional question aggregation mechanism to encode a question vector based on the important words in a question. During prediction, we incorporate sequence-level encoding of the first wh-word and its immediately following word as an additional source of question type information. Our proposed model achieves significant improvements over the best prior state-of-the-art results on three large-scale challenging QA datasets, namely NewsQA, TriviaQA, and SearchQA.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Open-Domain Question Answering | SearchQA | AMANDA | EM | - | #11 of 14 | Archive leaderboard | report |
| Open-Domain Question Answering | SearchQA | AMANDA | F1 | - | #11 of 14 | Archive leaderboard | report |
| Open-Domain Question Answering | SearchQA | AMANDA | N-gram F1 | 56.6 | #11 of 14 | Archive leaderboard | report |
| Open-Domain Question Answering | SearchQA | AMANDA | Unigram Acc | 46.8 | #11 of 14 | Archive leaderboard | report |
| Question Answering | NewsQA | AMANDA | EM | 48.4 | #14 of 18 | Archive leaderboard | report |
| Question Answering | NewsQA | AMANDA | F1 | 63.7 | #14 of 18 | 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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