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Found 6 papers, 1 papers with code
SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser
Structural priming is a widely used psycholinguistic paradigm to study human sentence representations.
Can training neural language models on a curriculum with developmentally plausible data improve alignment with human reading behavior?
While some work has found that the surprisal estimates from these models can be used to predict a wide range of human neural and behavioral responses, other work studying more complex syntactic phenomena has found that these surprisal estimates generate incorrect behavioral predictions.
Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction
We apply this method to study how BERT models of different sizes process relative clauses (RCs).
Dynabench: Rethinking Benchmarking in NLP
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking.
To what extent do human explanations of model behavior align with actual model behavior?
Given the increasingly prominent role NLP models (will) play in our lives, it is important for human expectations of model behavior to align with actual model behavior.
Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models
Neural language models (LMs) perform well on tasks that require sensitivity to syntactic structure.
