Syntactic Belief Update as the Driver of Garden Path Processing Difficulty
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Abstract (arXiv)
Garden path sentences present a processing difficulty for humans--- the sentence prefix leads the listener towards one interpretation, until the listener hears a critical word that shows that the initial interpretation was wrong. Lexical surprisal, a measure that usually predicts sentence processing difficulty quite well, fails to provide good predictions for garden path sentences. We propose an alternative that actively predicts a probability distribution over syntactic trees (its syntactic belief) and updates that distribution after each new word. If a processor is led down a garden path, syntactic beliefs will be wrong and will require a large update at the critical word. The magnitude of the update is measured with a generalized R'enyi divergence. Crucially, this metric is dependent on lexical items, but is fully independent of the probability of lexical items. This Syntactic Belief Update provides a better fit to the human reading time data on garden path sentences. This suggests a new research direction examining purely non-lexical alternatives to surprisal for psycholinguistics.
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References · 16
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
- 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
- 2022Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?cited by 113
- 2016Recurrent Neural Network Grammarscited by 109
- 2023Testing the Predictions of Surprisal Theory in 11 Languagescited by 77
- 2021Revisiting the Uniform Information Density Hypothesiscited by 68
- 2018Finding Syntax in Human Encephalography with Beam Searchcited by 41
- 2022Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguitiescited by 30
- 2020Efficient Computation of Expectations under Spanning Tree Distributionscited by 11
- 2024Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attentioncited by 11
- 2020A Cross-Genre Ensemble Approach to Robust Reddit Part of Speech Taggingcited by 9
4 more
- 2023Are UD Treebanks Getting More Consistent? A Report Card for English UDcited by 4
- 2022Unbiased and Efficient Sampling of Dependency Treescited by 2
- 2023SynJax: Structured Probability Distributions for JAXcited by 2
- 2026LLM surprisal is necessary but not sufficient to capture English garden-path effects: Evidence from joint latent modeling of reading paradigmscited by 1
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