TY - GEN
T1 - Modeling garden path effects without explicit hierarchical syntax
AU - van Schijndel, Marten
AU - Linzen, Tal
N1 - Publisher Copyright:
© 2018 Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018. All rights reserved.
PY - 2018
Y1 - 2018
N2 - The disambiguation of syntactically ambiguous sentences can lead to reading difficulty, often referred to as a garden path effect. The surprisal hypothesis suggests that this difficulty can be accounted for using word predictability. We tested this hypothesis using predictability estimates derived from two families of language models: grammar-based models, which explicitly encode the syntax of the language; and recurrent neural network (RNN) models, which do not. Both classes of models correctly predicted increased difficulty in ambiguous sentences compared to controls, suggesting that the syntactic representations induced by RNNs are sufficient for this purpose. At the same time, surprisal estimates derived from all models systematically underestimated the magnitude of the effect, and failed to predict the difference between easier (NP/S) and harder (NP/Z) ambiguities. This suggests that it may not be possible to reduce garden path effects to predictability.
AB - The disambiguation of syntactically ambiguous sentences can lead to reading difficulty, often referred to as a garden path effect. The surprisal hypothesis suggests that this difficulty can be accounted for using word predictability. We tested this hypothesis using predictability estimates derived from two families of language models: grammar-based models, which explicitly encode the syntax of the language; and recurrent neural network (RNN) models, which do not. Both classes of models correctly predicted increased difficulty in ambiguous sentences compared to controls, suggesting that the syntactic representations induced by RNNs are sufficient for this purpose. At the same time, surprisal estimates derived from all models systematically underestimated the magnitude of the effect, and failed to predict the difference between easier (NP/S) and harder (NP/Z) ambiguities. This suggests that it may not be possible to reduce garden path effects to predictability.
KW - garden path
KW - neural networks
KW - self-paced reading
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M3 - Conference contribution
AN - SCOPUS:85108564234
T3 - Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
SP - 2603
EP - 2608
BT - Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
PB - The Cognitive Science Society
T2 - 40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018
Y2 - 25 July 2018 through 28 July 2018
ER -