AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Context

TL;DR

This paper introduces AUTALIC, a novel dataset for detecting anti-autistic ableist language in context, highlighting current models' struggles and aiming to improve NLP inclusivity and understanding of neurodiversity.

Contribution

The creation of AUTALIC, the first benchmark dataset focused on anti-autistic ableist language detection in context, with expert annotations and comprehensive evaluation.

Findings

Current language models perform poorly on anti-autistic ableism detection.

Expert annotations reveal nuanced and context-dependent expressions of ableist language.

AUTALIC provides a valuable resource for advancing inclusive NLP research.

Abstract

As our understanding of autism and ableism continues to increase, so does our understanding of ableist language towards autistic people. Such language poses a significant challenge in NLP research due to its subtle and context-dependent nature. Yet, detecting anti-autistic ableist language remains underexplored, with existing NLP tools often failing to capture its nuanced expressions. We present AUTALIC, the first benchmark dataset dedicated to the detection of anti-autistic ableist language in context, addressing a significant gap in the field. The dataset comprises 2,400 autism-related sentences collected from Reddit, accompanied by surrounding context, and is annotated by trained experts with backgrounds in neurodiversity. Our comprehensive evaluation reveals that current language models, including state-of-the-art LLMs, struggle to reliably identify anti-autistic ableism and align with human judgments, underscoring their limitations in this domain. We publicly release AUTALIC along with the individual annotations which serve as a valuable resource to researchers working on ableism, neurodiversity, and also studying disagreements in annotation tasks. This dataset serves as a crucial step towards developing more inclusive and context-aware NLP systems that better reflect diverse perspectives.