Papers › Explicit Utilization of General Knowledge in Machine Reading Comprehension
Explicit Utilization of General Knowledge in Machine Reading Comprehension
Chao Wang, Hui Jiang
To bridge the gap between Machine Reading Comprehension (MRC) models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, in this paper, we explore how to integrate the neural networks of MRC models with the general knowledge of human beings. On the one hand, we propose a data enrichment method, which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. On the other hand, we propose an end-to-end MRC model named as Knowledge Aided Reader (KAR), which explicitly uses the above extracted general knowledge to assist its attention mechanisms. Based on the data enrichment method, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them. When only a subset (20%-80%) of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise.
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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 | KAR (single model) | EM | 76.125 | #113 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | KAR (single model) | F1 | 83.538 | #113 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | KAR | EM | 76.7 | #23 of 55 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | KAR | F1 | 84.9 | #23 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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