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        <title>Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension</title>
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        <namePart type="given">Minghao</namePart>
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    <name type="personal">
        <namePart type="given">Dongsheng</namePart>
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    <abstract>This paper considers the reading comprehension task in which multiple documents are given as input. Prior work has shown that a pipeline of retriever, reader, and reranker can improve the overall performance. However, the pipeline system is inefficient since the input is re-encoded within each module, and is unable to leverage upstream components to help downstream training. In this work, we present RE³QA, a unified question answering model that combines context retrieving, reading comprehension, and answer reranking to predict the final answer. Unlike previous pipelined approaches, RE³QA shares contextualized text representation across different components, and is carefully designed to use high-quality upstream outputs (e.g., retrieved context or candidate answers) for directly supervising downstream modules (e.g., the reader or the reranker). As a result, the whole network can be trained end-to-end to avoid the context inconsistency problem. Experiments show that our model outperforms the pipelined baseline and achieves state-of-the-art results on two versions of TriviaQA and two variants of SQuAD.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1221</identifier>
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%0 Conference Proceedings
%T Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension
%A Hu, Minghao
%A Peng, Yuxing
%A Huang, Zhen
%A Li, Dongsheng
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F hu-etal-2019-retrieve
%X This paper considers the reading comprehension task in which multiple documents are given as input. Prior work has shown that a pipeline of retriever, reader, and reranker can improve the overall performance. However, the pipeline system is inefficient since the input is re-encoded within each module, and is unable to leverage upstream components to help downstream training. In this work, we present RE³QA, a unified question answering model that combines context retrieving, reading comprehension, and answer reranking to predict the final answer. Unlike previous pipelined approaches, RE³QA shares contextualized text representation across different components, and is carefully designed to use high-quality upstream outputs (e.g., retrieved context or candidate answers) for directly supervising downstream modules (e.g., the reader or the reranker). As a result, the whole network can be trained end-to-end to avoid the context inconsistency problem. Experiments show that our model outperforms the pipelined baseline and achieves state-of-the-art results on two versions of TriviaQA and two variants of SQuAD.
%R 10.18653/v1/P19-1221
%U https://aclanthology.org/P19-1221
%U https://doi.org/10.18653/v1/P19-1221
%P 2285-2295

Markdown (Informal)

[Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension](https://aclanthology.org/P19-1221) (Hu et al., ACL 2019)

• Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension (Hu et al., ACL 2019)

ACL

• Minghao Hu, Yuxing Peng, Zhen Huang, and Dongsheng Li. 2019. Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2285–2295, Florence, Italy. Association for Computational Linguistics.