@inproceedings{jurczyk-choi-2017-cross,
    title = "Cross-genre Document Retrieval: Matching between Conversational and Formal Writings",
    author = "Jurczyk, Tomasz  and
      Choi, Jinho D.",
    editor = "Bender, Emily  and
      Daum{\'e} III, Hal  and
      Ettinger, Allyson  and
      Rao, Sudha",
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W17-5407/",
    doi = "10.18653/v1/W17-5407",
    pages = "48--53",
    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."
}