Papers › SG-Net: Syntax-Guided Machine Reading Comprehension
SG-Net: Syntax-Guided Machine Reading Comprehension
Zhuosheng Zhang, Yuwei Wu, Junru Zhou, Sufeng Duan, Hai Zhao, Rui Wang
For machine reading comprehension, the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy passages and getting ride of the noises is essential to improve its performance. Traditional attentive models attend to all words without explicit constraint, which results in inaccurate concentration on some dispensable words. In this work, we propose using syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanism for better linguistically motivated word representations. In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention. Syntax-guided network (SG-Net) is then composed of this extra SDOI-SAN and the SAN from the original Transformer encoder through a dual contextual architecture for better linguistics inspired representation. To verify its effectiveness, the proposed SG-Net is applied to typical pre-trained language model BERT which is right based on a Transformer encoder. Extensive experiments on popular benchmarks including SQuAD 2.0 and RACE show that the proposed SG-Net design helps achieve substantial performance improvement over strong baselines.
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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 | SQuAD2.0 | XLNet + SG-Net Verifier (ensemble) | EM | 88.174 | #63 of 286 | Archive leaderboard | report |
| Question Answering | SQuAD2.0 | XLNet + SG-Net Verifier (ensemble) | F1 | 90.702 | #63 of 286 | Archive leaderboard | report |
| Question Answering | SQuAD2.0 | XLNet + SG-Net Verifier++ (single model) | EM | 87.238 | #85 of 286 | Archive leaderboard | report |
| Question Answering | SQuAD2.0 | XLNet + SG-Net Verifier++ (single model) | F1 | 90.071 | #85 of 286 | Archive leaderboard | report |
| Question Answering | SQuAD2.0 | SG-Net (ensemble) | EM | 86.211 | #103 of 286 | Archive leaderboard | report |
| Question Answering | SQuAD2.0 | SG-Net (ensemble) | F1 | 88.848 | #103 of 286 | Archive leaderboard | report |
| Question Answering | SQuAD2.0 | SG-Net (single model) | EM | 85.229 | #123 of 286 | Archive leaderboard | report |
| Question Answering | SQuAD2.0 | SG-Net (single model) | F1 | 87.926 | #123 of 286 | Archive leaderboard | report |
| Question Answering | SQuAD2.0 dev | SG-Net | EM | 85.1 | #5 of 13 | Archive leaderboard | report |
| Question Answering | SQuAD2.0 dev | SG-Net | F1 | 87.9 | #5 of 13 | 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.
Methods
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