Papers with human language experiments

1 papers
What BERT Is Not: Lessons from a New Suite of Psycholinguistic Diagnostics for Language Models (2020.tacl-1)

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Challenge: Pretraining by language modeling has become popular but we have yet to understand what language models learn during that process.
Approach: They propose diagnostics that ask questions about information used by language models for generating predictions in context.
Outcome: The proposed diagnostics can be used to study the popular BERT model . they show that the model can distinguish good from bad completions, but struggles with inference and role-based event prediction.

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