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        <title>Multi-Hop Paragraph Retrieval for Open-Domain Question Answering</title>
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    <abstract>This paper is concerned with the task of multi-hop open-domain Question Answering (QA). This task is particularly challenging since it requires the simultaneous performance of textual reasoning and efficient searching. We present a method for retrieving multiple supporting paragraphs, nested amidst a large knowledge base, which contain the necessary evidence to answer a given question. Our method iteratively retrieves supporting paragraphs by forming a joint vector representation of both a question and a paragraph. The retrieval is performed by considering contextualized sentence-level representations of the paragraphs in the knowledge source. Our method achieves state-of-the-art performance over two well-known datasets, SQuAD-Open and HotpotQA, which serve as our single- and multi-hop open-domain QA benchmarks, respectively.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1222</identifier>
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%0 Conference Proceedings
%T Multi-Hop Paragraph Retrieval for Open-Domain Question Answering
%A Feldman, Yair
%A El-Yaniv, Ran
%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 feldman-el-yaniv-2019-multi
%X This paper is concerned with the task of multi-hop open-domain Question Answering (QA). This task is particularly challenging since it requires the simultaneous performance of textual reasoning and efficient searching. We present a method for retrieving multiple supporting paragraphs, nested amidst a large knowledge base, which contain the necessary evidence to answer a given question. Our method iteratively retrieves supporting paragraphs by forming a joint vector representation of both a question and a paragraph. The retrieval is performed by considering contextualized sentence-level representations of the paragraphs in the knowledge source. Our method achieves state-of-the-art performance over two well-known datasets, SQuAD-Open and HotpotQA, which serve as our single- and multi-hop open-domain QA benchmarks, respectively.
%R 10.18653/v1/P19-1222
%U https://aclanthology.org/P19-1222
%U https://doi.org/10.18653/v1/P19-1222
%P 2296-2309

Markdown (Informal)

[Multi-Hop Paragraph Retrieval for Open-Domain Question Answering](https://aclanthology.org/P19-1222) (Feldman & El-Yaniv, ACL 2019)

• Multi-Hop Paragraph Retrieval for Open-Domain Question Answering (Feldman & El-Yaniv, ACL 2019)

ACL

• Yair Feldman and Ran El-Yaniv. 2019. Multi-Hop Paragraph Retrieval for Open-Domain Question Answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2296–2309, Florence, Italy. Association for Computational Linguistics.