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

• URL: http://arxiv.org/abs/2410.16520v1
• Date: Mon, 21 Oct 2024 21:21:29 GMT
• Title: AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Context
• Authors: Naba Rizvi, Harper Strickland, Daniel Gitelman, Tristan Cooper, Alexis Morales-Flores, Michael Golden, Aekta Kallepalli, Akshat Alurkar, Haaset Owens, Saleha Ahmedi, Isha Khirwadkar, Imani Munyaka, Nedjma Ousidhoum,
• Abstract summary: AUTALIC is the first benchmark dataset dedicated to the detection of anti-autistic ableist language in context. 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.
• Score: 1.3334268990558924
• License:
• 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.

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