Papers › Words or Characters? Fine-grained Gating for Reading Comprehension
Words or Characters? Fine-grained Gating for Reading Comprehension
Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen, Ruslan Salakhutdinov
Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension. We present a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words. We also extend the idea of fine-grained gating to modeling the interaction between questions and paragraphs for reading comprehension. Experiments show that our approach can improve the performance on reading comprehension tasks, achieving new state-of-the-art results on the Children's Book Test dataset. To demonstrate the generality of our gating mechanism, we also show improved results on a social media tag prediction task.
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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 | Fine-Grained Gating | EM | 62.446 | #188 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | Fine-Grained Gating | F1 | 73.327 | #188 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | FG fine-grained gate | EM | 59.95 | #50 of 55 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | FG fine-grained gate | F1 | 71.25 | #50 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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