Papers › FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen, Weizhu Chen
This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. First, it puts forward a novel concept of "history of word" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation. Second, it introduces an improved attention scoring function that better utilizes the "history of word" concept. Third, it proposes a fully-aware multi-level attention mechanism to capture the complete information in one text (such as a question) and exploit it in its counterpart (such as context or passage) layer by layer. We apply FusionNet to the Stanford Question Answering Dataset (SQuAD) and it achieves the first position for both single and ensemble model on the official SQuAD leaderboard at the time of writing (Oct. 4th, 2017). Meanwhile, we verify the generalization of FusionNet with two adversarial SQuAD datasets and it sets up the new state-of-the-art on both datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to 51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | SQuAD1.1 | FusionNet (ensemble) | EM | 78.978 | #80 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | FusionNet (ensemble) | F1 | 86.016 | #80 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | FusionNet (single model) | EM | 75.968 | #115 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | FusionNet (single model) | F1 | 83.900 | #115 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | FusionNet | EM | 75.3 | #26 of 55 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | FusionNet | F1 | 83.6 | #26 of 55 | Archive leaderboard | report |
| Question Answering | SQuAD2.0 | FusionNet++ (ensemble) | EM | 70.300 | #252 of 286 | Archive leaderboard | report |
| Question Answering | SQuAD2.0 | FusionNet++ (ensemble) | F1 | 72.484 | #252 of 286 | 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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