{"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/interpretation-of-natural-language-rules-in","title":"Interpretation of Natural Language Rules in Conversational Machine Reading","arxiv_id":"1809.01494","date":"2018-08-28","proceeding":"EMNLP 2018 10","authors":["Marzieh Saeidi","Max Bartolo","Patrick Lewis","Sameer Singh","Tim Rocktäschel","Mike Sheldon","Guillaume Bouchard","Sebastian Riedel"],"abstract":"Most work in machine reading focuses on question answering problems where the\nanswer is directly expressed in the text to read. However, many real-world\nquestion answering problems require the reading of text not because it contains\nthe literal answer, but because it contains a recipe to derive an answer\ntogether with the reader's background knowledge. One example is the task of\ninterpreting regulations to answer \"Can I...?\" or \"Do I have to...?\" questions\nsuch as \"I am working in Canada. Do I have to carry on paying UK National\nInsurance?\" after reading a UK government website about this topic. This task\nrequires both the interpretation of rules and the application of background\nknowledge. It is further complicated due to the fact that, in practice, most\nquestions are underspecified, and a human assistant will regularly have to ask\nclarification questions such as \"How long have you been working abroad?\" when\nthe answer cannot be directly derived from the question and text. In this\npaper, we formalise this task and develop a crowd-sourcing strategy to collect\n32k task instances based on real-world rules and crowd-generated questions and\nscenarios. We analyse the challenges of this task and assess its difficulty by\nevaluating the performance of rule-based and machine-learning baselines. We\nobserve promising results when no background knowledge is necessary, and\nsubstantial room for improvement whenever background knowledge is needed.","url_abs":"http://arxiv.org/abs/1809.01494v1","url_pdf":"http://arxiv.org/pdf/1809.01494v1.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":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[{"method_slug":"am","method_name":"AM"}],"datasets_introduced":[{"slug":"sharc","name":"ShARC","full_name":"Shaping Answers with Rules through Conversation"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.01494","atlas_url":"https://app.syntology.ai/?focus=1809.01494","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}