Abstract
This paper addresses the problem of question answering style multi-passage Machine Reading Comprehension (MRC) and suggests that paragraph-level segments are suitable to answer questions in real Web query scenario. We propose to combine a learning to rank framework with an attention-based neural network to select the best-matching paragraph for a specific question. To estimate the quality of a paragraph with respect to a given query, its largest ROUGE-L score compared against the annotated answers is used as the ranking indicator. Experimental results on a real-world dataset demonstrate that the proposed method obtains a significant improvement compared to the state-of-the-art baselines.
This work was supported in part by the National Key Research and Development Program of China (2018YFB1004502) and in part by the National Natural Science Foundation of China (61532001).
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Lin, D., Tang, J., Pang, K., Li, S., Wang, T. (2019). Selecting Paragraphs to Answer Questions for Multi-passage Machine Reading Comprehension. In: Zhang, Q., Liao, X., Ren, Z. (eds) Information Retrieval. CCIR 2019. Lecture Notes in Computer Science(), vol 11772. Springer, Cham. https://doi.org/10.1007/978-3-030-31624-2_10
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