Papers › DUMA: Reading Comprehension with Transposition Thinking
DUMA: Reading Comprehension with Transposition Thinking
Pengfei Zhu, Hai Zhao, Xiaoguang Li
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question. Thus in addition to a powerful Pre-trained Language Model (PrLM) as encoder, multi-choice MRC especially relies on a matching network design which is supposed to effectively capture the relationships among the triplet of passage, question and answers. While the newer and more powerful PrLMs have shown their mightiness even without the support from a matching network, we propose a new DUal Multi-head Co-Attention (DUMA) model, which is inspired by human's transposition thinking process solving the multi-choice MRC problem: respectively considering each other's focus from the standpoint of passage and question. The proposed DUMA has been shown effective and is capable of generally promoting PrLMs. Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM and RACE, showing that in terms of powerful PrLMs, DUMA can still boost the model to reach new state-of-the-art performance.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Reading Comprehension | RACE | ALBERTxxlarge+DUMA(ensemble) | Accuracy | 89.8 | #3 of 24 | Archive leaderboard | report |
| Reading Comprehension | RACE | ALBERTxxlarge+DUMA(ensemble) | Accuracy (High) | 92.6 | #3 of 24 | Archive leaderboard | report |
| Reading Comprehension | RACE | ALBERTxxlarge+DUMA(ensemble) | Accuracy (Middle) | 88.7 | #3 of 24 | 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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