{"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/neural-domain-adaptation-for-biomedical","title":"Neural Domain Adaptation for Biomedical Question Answering","arxiv_id":"1706.03610","date":"2017-06-12","proceeding":"CONLL 2017 8","authors":["Georg Wiese","Dirk Weissenborn","Mariana Neves"],"abstract":"Factoid question answering (QA) has recently benefited from the development\nof deep learning (DL) systems. Neural network models outperform traditional\napproaches in domains where large datasets exist, such as SQuAD (ca. 100,000\nquestions) for Wikipedia articles. However, these systems have not yet been\napplied to QA in more specific domains, such as biomedicine, because datasets\nare generally too small to train a DL system from scratch. For example, the\nBioASQ dataset for biomedical QA comprises less then 900 factoid (single\nanswer) and list (multiple answers) QA instances. In this work, we adapt a\nneural QA system trained on a large open-domain dataset (SQuAD, source) to a\nbiomedical dataset (BioASQ, target) by employing various transfer learning\ntechniques. Our network architecture is based on a state-of-the-art QA system,\nextended with biomedical word embeddings and a novel mechanism to answer list\nquestions. In contrast to existing biomedical QA systems, our system does not\nrely on domain-specific ontologies, parsers or entity taggers, which are\nexpensive to create. Despite this fact, our systems achieve state-of-the-art\nresults on factoid questions and competitive results on list questions.","url_abs":"http://arxiv.org/abs/1706.03610v2","url_pdf":"http://arxiv.org/pdf/1706.03610v2.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":"neural-domain-adaptation-for-biomedical","repo_url":"https://github.com/georgwiese/biomedical-qa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1706.03610","atlas_url":"https://app.syntology.ai/?focus=1706.03610","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}