{"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/lexicosyntactic-inference-in-neural-models","title":"Lexicosyntactic Inference in Neural Models","arxiv_id":"1808.06232","date":"2018-08-19","proceeding":"EMNLP 2018 10","authors":["Aaron Steven White","Rachel Rudinger","Kyle Rawlins","Benjamin Van Durme"],"abstract":"We investigate neural models' ability to capture lexicosyntactic inferences:\ninferences triggered by the interaction of lexical and syntactic information.\nWe take the task of event factuality prediction as a case study and build a\nfactuality judgment dataset for all English clause-embedding verbs in various\nsyntactic contexts. We use this dataset, which we make publicly available, to\nprobe the behavior of current state-of-the-art neural systems, showing that\nthese systems make certain systematic errors that are clearly visible through\nthe lens of factuality prediction.","url_abs":"http://arxiv.org/abs/1808.06232v1","url_pdf":"http://arxiv.org/pdf/1808.06232v1.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":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[{"slug":"megaveridicality","name":"MegaVeridicality","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1808.06232","atlas_url":"https://app.syntology.ai/?focus=1808.06232","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}