{"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/implicit-argument-prediction-as-reading","title":"Implicit Argument Prediction as Reading Comprehension","arxiv_id":"1811.03554","date":"2018-11-08","proceeding":null,"authors":["Pengxiang Cheng","Katrin Erk"],"abstract":"Implicit arguments, which cannot be detected solely through syntactic cues,\nmake it harder to extract predicate-argument tuples. We present a new model for\nimplicit argument prediction that draws on reading comprehension, casting the\npredicate-argument tuple with the missing argument as a query. We also draw on\npointer networks and multi-hop computation. Our model shows good performance on\nan argument cloze task as well as on a nominal implicit argument prediction\ntask.","url_abs":"http://arxiv.org/abs/1811.03554v1","url_pdf":"http://arxiv.org/pdf/1811.03554v1.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":"implicit-argument-prediction-as-reading","repo_url":"https://github.com/pxch/imp_arg_rc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}