{"pk":28176,"title":"Modeling garden path effects without explicit hierarchical syntax","subtitle":null,"abstract":"The disambiguation of syntactically ambiguous sentences canlead to reading difficulty, often referred to as a garden path ef-fect. The surprisal hypothesis suggests that this difficulty canbe accounted for using word predictability. We tested this hy-pothesis using predictability estimates derived from two fam-ilies of language models: grammar-based models, which ex-plicitly encode the syntax of the language; and recurrent neuralnetwork (RNN) models, which do not. Both classes of mod-els correctly predicted increased difficulty in ambiguous sen-tences compared to controls, suggesting that the syntactic rep-resentations induced by RNNs are sufficient for this purpose.At the same time, surprisal estimates derived from all mod-els systematically underestimated the magnitude of the effect,and failed to predict the difference between easier (NP/S) andharder (NP/Z) ambiguities. This suggests that it may not bepossible to reduce garden path effects to predictability","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"self-paced reading; garden path; neural networks"}],"section":"Publication-based-Talks","is_remote":true,"remote_url":"https://escholarship.org/uc/item/7mg5442r","frozenauthors":[{"first_name":"Marten","middle_name":"","last_name":"van Schijndel","name_suffix":"","institution":"John Hopkins","department":""},{"first_name":"Tal","middle_name":"","last_name":"Linzen","name_suffix":"","institution":"John Hopkins","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2018-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/28176/galley/17835/download/"}]}