2022/04/29 by Antonio Laverghetta, Antonio Laverghetta Jr., Laverghetta, Antonio +2
Computer Science · Psychology · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Language Development and Disorders #Natural Language Processing Techniques #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2204.14114
To appear as a short paper at ACL 2022
arxiv created 2022/04/29 · openalex publication_date 2022/04/29 · arxiv updated 2022/05/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Reasoning using negation is known to be difficult for transformer-based language models. While previous studies have used the tools of psycholinguistics to probe a transformer's ability to reason over negation, none have focused on the types of negation studied in developmental psychology. We explore how well transformers can process such categories of negation, by framing the problem as a natural language inference (NLI) task. We curate a set of diagnostic questions for our target categories from popular NLI datasets and evaluate how well a suite of models reason over them. We find that models perform consistently better only on certain categories, suggesting clear distinctions in how they are processed.