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Found 12 papers, 5 papers with code
Testing the Predictions of Surprisal Theory in 11 Languages
We address this gap in the current literature by investigating the relationship between surprisal and reading times in eleven different languages, distributed across five language families.
Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars
We study grammar induction with mildly context-sensitive grammars for unsupervised discontinuous parsing.
Probing for Incremental Parse States in Autoregressive Language Models
This suggests implicit incremental syntactic inferences underlie next-word predictions in autoregressive neural language models.
How Adults Understand What Young Children Say
Children's early speech often bears little resemblance to that of adults, and yet parents and other caregivers are able to interpret that speech and react accordingly.
Grammar-Based Grounded Lexicon Learning
We present Grammar-Based Grounded Lexicon Learning (G2L2), a lexicalist approach toward learning a compositional and grounded meaning representation of language from grounded data, such as paired images and texts.
A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humans
We present a targeted, scaled-up comparison of incremental processing in humans and neural language models by collecting by-word reaction time data for sixteen different syntactic test suites across a range of structural phenomena.
Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication
How do adults understand children's speech?
A Rate-Distortion view of human pragmatic reasoning
What computational principles underlie human pragmatic reasoning?
A Systematic Assessment of Syntactic Generalization in Neural Language Models
While state-of-the-art neural network models continue to achieve lower perplexity scores on language modeling benchmarks, it remains unknown whether optimizing for broad-coverage predictive performance leads to human-like syntactic knowledge.
Do RNNs learn human-like abstract word order preferences?
We collect human acceptability ratings for our stimuli, in the first acceptability judgment experiment directly manipulating the predictors of syntactic alternations.
Modeling the effects of memory on human online sentence processing with particle filters
Language comprehension in humans is significantly constrained by memory, yet rapid, highly incremental, and capable of utilizing a wide range of contextual information to resolve ambiguity and form expectations about future input.
