Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension (P18-1)
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| Challenge: | Various advanced neural models have been proposed for reading comprehension, but most models ignore its relations with other answer candidates. |
| Approach: | They propose to model reading comprehension as an extract-then-select two-stage procedure . they first extract answer candidates from passages, then select the final answer by combining information from all candidates. |
| Outcome: | The proposed approach improves state-of-the-art performance on open-domain reading comprehension datasets. |
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| Challenge: | Existing methods to predict the start and end positions of answer spans generate two probability vectors. |
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| Challenge: | Recent work on open-domain question answering focuses on either extractive or generative readers exclusively. |
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