{"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/rnns-as-psycholinguistic-subjects-syntactic","title":"RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency","arxiv_id":"1809.01329","date":"2018-09-05","proceeding":null,"authors":["Richard Futrell","Ethan Wilcox","Takashi Morita","Roger Levy"],"abstract":"Recurrent neural networks (RNNs) are the state of the art in sequence\nmodeling for natural language. However, it remains poorly understood what\ngrammatical characteristics of natural language they implicitly learn and\nrepresent as a consequence of optimizing the language modeling objective. Here\nwe deploy the methods of controlled psycholinguistic experimentation to shed\nlight on to what extent RNN behavior reflects incremental syntactic state and\ngrammatical dependency representations known to characterize human linguistic\nbehavior. We broadly test two publicly available long short-term memory (LSTM)\nEnglish sequence models, and learn and test a new Japanese LSTM. We demonstrate\nthat these models represent and maintain incremental syntactic state, but that\nthey do not always generalize in the same way as humans. Furthermore, none of\nour models learn the appropriate grammatical dependency configurations\nlicensing reflexive pronouns or negative polarity items.","url_abs":"http://arxiv.org/abs/1809.01329v1","url_pdf":"http://arxiv.org/pdf/1809.01329v1.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":"rnns-as-psycholinguistic-subjects-syntactic","repo_url":"https://github.com/Futrell/rnn_psycholinguistic_subjects","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.01329","atlas_url":"https://app.syntology.ai/?focus=1809.01329","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}