Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities

• URL: http://arxiv.org/abs/2210.12187v2
• Date: Tue, 1 Aug 2023 22:23:09 GMT
• Title: Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities
• Authors: Suhas Arehalli, Brian Dillon, Tal Linzen
• Abstract summary: We propose a method for estimating syntactic predictability from a language model. We find that treating syntactic predictability independently from lexical predictability indeed results in larger estimates of garden path effects. Our results support the hypothesis that predictability is not the only factor responsible for the processing cost associated with garden path sentences.
• Score: 19.659811811023374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans exhibit garden path effects: When reading sentences that are temporarily structurally ambiguous, they slow down when the structure is disambiguated in favor of the less preferred alternative. Surprisal theory (Hale, 2001; Levy, 2008), a prominent explanation of this finding, proposes that these slowdowns are due to the unpredictability of each of the words that occur in these sentences. Challenging this hypothesis, van Schijndel & Linzen (2021) find that estimates of the cost of word predictability derived from language models severely underestimate the magnitude of human garden path effects. In this work, we consider whether this underestimation is due to the fact that humans weight syntactic factors in their predictions more highly than language models do. We propose a method for estimating syntactic predictability from a language model, allowing us to weigh the cost of lexical and syntactic predictability independently. We find that treating syntactic predictability independently from lexical predictability indeed results in larger estimates of garden path. At the same time, even when syntactic predictability is independently weighted, surprisal still greatly underestimate the magnitude of human garden path effects. Our results support the hypothesis that predictability is not the only factor responsible for the processing cost associated with garden path sentences.

Related papers

• SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser [24.613169009428827]
  We propose a framework for using empirical priming patterns to build a theory characterizing the structural representations humans construct when processing sentences. We use SPAWN to generate priming predictions from two theoretical accounts which make different assumptions about the structure of relative clauses. We find that the predictions from only one of these theories align with empirical priming patterns, thus highlighting which assumptions about relative clause better capture human sentence representations.
  arXiv  Detail & Related papers  (2024-03-11T22:58:58Z)
• PROPRES: Investigating the Projectivity of Presupposition with Various Triggers and Environments [13.896697187967547]
  We introduce a new dataset, projectivity of presupposition (PROPRES) Our human evaluation reveals that humans exhibit variable projectivity in some cases. Our findings suggest that probing studies on pragmatic inferences should take extra care of the human judgment variability.
  arXiv  Detail & Related papers  (2023-12-14T09:07:57Z)
• Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the "right reasons"? [15.773775387121097]
  We show that calibration of large language models typically improves with model size. We find that temperature-scaling probabilities lead to a systematically better fit to reading times.
  arXiv  Detail & Related papers  (2023-11-15T19:34:06Z)
• Humans and language models diverge when predicting repeating text [52.03471802608112]
  We present a scenario in which the performance of humans and LMs diverges. Human and GPT-2 LM predictions are strongly aligned in the first presentation of a text span, but their performance quickly diverges when memory begins to play a role. We hope that this scenario will spur future work in bringing LMs closer to human behavior.
  arXiv  Detail & Related papers  (2023-10-10T08:24:28Z)
• Testing the Predictions of Surprisal Theory in 11 Languages [71.0450229199313]
  We investigate the relationship between surprisal and reading times in eleven different languages. By focusing on a more diverse set of languages, we argue that these results offer the most robust link to-date between information theory and incremental language processing across languages.
  arXiv  Detail & Related papers  (2023-07-07T15:37:50Z)
• Naturalistic Causal Probing for Morpho-Syntax [76.83735391276547]
  We suggest a naturalistic strategy for input-level intervention on real world data in Spanish. Using our approach, we isolate morpho-syntactic features from counfounders in sentences. We apply this methodology to analyze causal effects of gender and number on contextualized representations extracted from pre-trained models.
  arXiv  Detail & Related papers  (2022-05-14T11:47:58Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution [10.73926355134268]
  We study the dynamics of referring expressions using novel computational estimates of referent predictability. A statistical analysis of the relationship between model output and mention form supports the hypothesis that predictability affects the form of a mention.
  arXiv  Detail & Related papers  (2021-09-27T14:54:46Z)
• Double Robust Representation Learning for Counterfactual Prediction [68.78210173955001]
  We propose a novel scalable method to learn double-robust representations for counterfactual predictions. We make robust and efficient counterfactual predictions for both individual and average treatment effects. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
  arXiv  Detail & Related papers  (2020-10-15T16:39:26Z)
• Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans [75.15855405318855]
  We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing. Although the network was solely trained to predict the next word in a large corpus, analysis showed the emergence of specialized units that successfully handled local and long-distance syntactic agreement. We tested the model's predictions in a behavioral experiment where humans detected violations in number agreement in sentences with systematic variations in the singular/plural status of multiple nouns.
  arXiv  Detail & Related papers  (2020-06-19T12:00:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.