Papers › End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
Yang Yu, Wei zhang, Kazi Hasan, Mo Yu, Bing Xiang, Bo-Wen Zhou
This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions. DCR is able to predict answers of variable lengths, whereas previous neural RC models primarily focused on predicting single tokens or entities. DCR encodes a document and an input question with recurrent neural networks, and then applies a word-by-word attention mechanism to acquire question-aware representations for the document, followed by the generation of chunk representations and a ranking module to propose the top-ranked chunk as the answer. Experimental results show that DCR achieves state-of-the-art exact match and F1 scores on the SQuAD dataset.
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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 | Dynamic Chunk Reader | EM | 62.499 | #187 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | Dynamic Chunk Reader | F1 | 70.956 | #187 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | DCR | EM | 62.5 | #49 of 55 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | DCR | F1 | 71.2 | #49 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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