{"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/careful-selection-of-knowledge-to-solve-open","title":"Careful Selection of Knowledge to solve Open Book Question Answering","arxiv_id":"1907.10738","date":"2019-07-24","proceeding":"ACL 2019 7","authors":["Pratyay Banerjee","Kuntal Kumar Pal","Arindam Mitra","Chitta Baral"],"abstract":"Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic. Recently a challenge involving such QA, OpenBookQA, has been proposed. Unlike most other NLQA tasks that focus on linguistic understanding, OpenBookQA requires deeper reasoning involving linguistic understanding as well as reasoning with common knowledge. In this paper we address QA with respect to the OpenBookQA dataset and combine state of the art language models with abductive information retrieval (IR), information gain based re-ranking, passage selection and weighted scoring to achieve 72.0% accuracy, an 11.6% improvement over the current state of the art.","url_abs":"https://arxiv.org/abs/1907.10738v1","url_pdf":"https://arxiv.org/pdf/1907.10738v1.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":[],"tasks":[{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"re-ranking","task_name":"Re-Ranking"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-openbookqa","task":"Question Answering","dataset":"OpenBookQA","model":"Careful Selection","rank_in_archive_order":27,"of":45,"metrics":{"Accuracy":"72"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1907.10738","atlas_url":"https://app.syntology.ai/?focus=1907.10738","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}