{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-relevance-ranking-using-enhanced","title":"Deep Relevance Ranking Using Enhanced Document-Query Interactions","arxiv_id":"1809.01682","date":"2018-09-05","proceeding":"EMNLP 2018 10","authors":["Ryan McDonald","Georgios-Ioannis Brokos","Ion Androutsopoulos"],"abstract":"We explore several new models for document relevance ranking, building upon\nthe Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM,\nwhich uses context-insensitive encodings of terms and query-document term\ninteractions, we inject rich context-sensitive encodings throughout our models,\ninspired by PACRR's (Hui et al., 2017) convolutional n-gram matching features,\nbut extended in several ways including multiple views of query and document\ninputs. We test our models on datasets from the BIOASQ question answering\nchallenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005),\nshowing they outperform BM25-based baselines, DRMM, and PACRR.","url_abs":"http://arxiv.org/abs/1809.01682v2","url_pdf":"http://arxiv.org/pdf/1809.01682v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-relevance-ranking-using-enhanced","repo_url":"https://github.com/nlpaueb/deep-relevance-ranking","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"ad-hoc-information-retrieval","task_name":"Ad-Hoc Information Retrieval"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/ad-hoc-information-retrieval-on-trec-robust04","task":"Ad-Hoc Information Retrieval","dataset":"TREC Robust04","model":"POSIT-DRMM-MV","rank_in_archive_order":7,"of":21,"metrics":{"MAP":"0.271","P@20":"0.389","nDCG@20":"0.464"},"uses_additional_data":false},{"leaderboard":"/sota/ad-hoc-information-retrieval-on-trec-robust04","task":"Ad-Hoc Information Retrieval","dataset":"TREC Robust04","model":"PACRR","rank_in_archive_order":10,"of":21,"metrics":{"MAP":"0.258","P@20":"0.374","nDCG@20":"0.445"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.01682","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}