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Award ID contains: 2402876

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  1. ; (, ACM)
    Autism is a neurodevelopmental disability that impacts one’s social communication and interaction. When left unsupported, this can increase the amount of loneliness felt by autistic people. Communication technology, such as AAC, can be helpful in supporting social communication, especially when co-designed with autistic people. We conducted a series of design workshops to co-design a new AAC system specifically supporting social communication. In this paper, we focus on the accessibility issues that were identified when running our workshops and provide recommendations on how to improve the process. We found that it is critical to build support for information processing time into the workshops, include a variety of AAC stakeholders, and create a shared vocabulary between the workshop participants to make design workshops more accessible to autistic adults. 
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    Free, publicly-accessible full text available October 26, 2026
  2. ; (, Association for Computational Linguistics)
    Users of Augmentative and Alternative Communication (AAC) may write letter-by-letter via an interface that uses a character language model. However, most state-of-the-art large pretrained language models predict subword tokens of variable length. We investigate how to practically use such models to make accurate and efficient character predictions. Our algorithm for producing character predictions from a subword large language model (LLM) provides more accurate predictions than using a classification layer, a byte-level LLM, or an n-gram model. Additionally, we investigate a domain adaptation procedure based on a large dataset of sentences we curated based on scoring how useful each sentence might be for spoken or written AAC communication. We find our procedure further improves model performance on simple, conversational text. 
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    Full Text Available