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        <title>Cross-genre Document Retrieval: Matching between Conversational and Formal Writings</title>
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            <namePart type="given">Emily</namePart>
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    <abstract>This paper challenges a cross-genre document retrieval task, where the queries are in formal writing and the target documents are in conversational writing. In this task, a query, is a sentence extracted from either a summary or a plot of an episode in a TV show, and the target document consists of transcripts from the corresponding episode. To establish a strong baseline, we employ the current state-of-the-art search engine to perform document retrieval on the dataset collected for this work. We then introduce a structure reranking approach to improve the initial ranking by utilizing syntactic and semantic structures generated by NLP tools. Our evaluation shows an improvement of more than 4% when the structure reranking is applied, which is very promising.</abstract>
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%0 Conference Proceedings
%T Cross-genre Document Retrieval: Matching between Conversational and Formal Writings
%A Jurczyk, Tomasz
%A Choi, Jinho D.
%Y Bender, Emily
%Y Daumé III, Hal
%Y Ettinger, Allyson
%Y Rao, Sudha
%S Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F jurczyk-choi-2017-cross
%X This paper challenges a cross-genre document retrieval task, where the queries are in formal writing and the target documents are in conversational writing. In this task, a query, is a sentence extracted from either a summary or a plot of an episode in a TV show, and the target document consists of transcripts from the corresponding episode. To establish a strong baseline, we employ the current state-of-the-art search engine to perform document retrieval on the dataset collected for this work. We then introduce a structure reranking approach to improve the initial ranking by utilizing syntactic and semantic structures generated by NLP tools. Our evaluation shows an improvement of more than 4% when the structure reranking is applied, which is very promising.
%R 10.18653/v1/W17-5407
%U https://aclanthology.org/W17-5407
%U https://doi.org/10.18653/v1/W17-5407
%P 48-53

Markdown (Informal)

[Cross-genre Document Retrieval: Matching between Conversational and Formal Writings](https://aclanthology.org/W17-5407) (Jurczyk & Choi, 2017)

• Cross-genre Document Retrieval: Matching between Conversational and Formal Writings (Jurczyk & Choi, 2017)

ACL

• Tomasz Jurczyk and Jinho D. Choi. 2017. Cross-genre Document Retrieval: Matching between Conversational and Formal Writings. In Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems, pages 48–53, Copenhagen, Denmark. Association for Computational Linguistics.