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        <namePart type="given">Seongjun</namePart>
        <namePart type="family">Yun</namePart>
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    <name type="personal">
        <namePart type="given">Hyunjae</namePart>
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    <name type="personal">
        <namePart type="given">Miyoung</namePart>
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    <name type="personal">
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    <abstract>Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source. As open-domain QA requires retrieving relevant documents from text corpora to answer questions, its performance largely depends on the performance of document retrievers. However, since traditional information retrieval systems are not effective in obtaining documents with a high probability of containing answers, they lower the performance of QA systems. Simply extracting more documents increases the number of irrelevant documents, which also degrades the performance of QA systems. In this paper, we introduce Paragraph Ranker which ranks paragraphs of retrieved documents for a higher answer recall with less noise. We show that ranking paragraphs and aggregating answers using Paragraph Ranker improves performance of open-domain QA pipeline on the four open-domain QA datasets by 7.8% on average.</abstract>
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%0 Conference Proceedings
%T Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering
%A Lee, Jinhyuk
%A Yun, Seongjun
%A Kim, Hyunjae
%A Ko, Miyoung
%A Kang, Jaewoo
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F lee-etal-2018-ranking
%X Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source. As open-domain QA requires retrieving relevant documents from text corpora to answer questions, its performance largely depends on the performance of document retrievers. However, since traditional information retrieval systems are not effective in obtaining documents with a high probability of containing answers, they lower the performance of QA systems. Simply extracting more documents increases the number of irrelevant documents, which also degrades the performance of QA systems. In this paper, we introduce Paragraph Ranker which ranks paragraphs of retrieved documents for a higher answer recall with less noise. We show that ranking paragraphs and aggregating answers using Paragraph Ranker improves performance of open-domain QA pipeline on the four open-domain QA datasets by 7.8% on average.
%R 10.18653/v1/D18-1053
%U https://aclanthology.org/D18-1053
%U https://doi.org/10.18653/v1/D18-1053
%P 565-569

Markdown (Informal)

[Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering](https://aclanthology.org/D18-1053) (Lee et al., EMNLP 2018)

• Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering (Lee et al., EMNLP 2018)

ACL

• Jinhyuk Lee, Seongjun Yun, Hyunjae Kim, Miyoung Ko, and Jaewoo Kang. 2018. Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 565–569, Brussels, Belgium. Association for Computational Linguistics.