@inproceedings{pal-etal-2024-empowering,
    title = "Empowering {AAC} Users: A Systematic Integration of Personal Narratives with Conversational {AI}",
    author = "Pal, Sayantan  and
      Das, Souvik  and
      Srihari, Rohini  and
      Higginborham, Jeff  and
      Bizovi, Jenna",
    editor = "Kumar, Sachin  and
      Balachandran, Vidhisha  and
      Park, Chan Young  and
      Shi, Weijia  and
      Hayati, Shirley Anugrah  and
      Tsvetkov, Yulia  and
      Smith, Noah  and
      Hajishirzi, Hannaneh  and
      Kang, Dongyeop  and
      Jurgens, David",
    booktitle = "Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.customnlp4u-1.2/",
    doi = "10.18653/v1/2024.customnlp4u-1.2",
    pages = "12--25",
    abstract = "Communication barriers have long posed challenges for users of Alternate and Augmentative Communication (AAC). In AAC, effective conversational aids are not solely about harnessing Artificial Intelligence (AI) capabilities but more about ensuring these technologies resonate deeply with AAC user{'}s unique communication challenges. We aim to bridge the gap between generic outputs and genuine human interactions by integrating advanced Conversational AI with personal narratives. While existing solutions offer generic responses, a considerable gap in tailoring outputs reflecting an AAC user{'}s intent must be addressed. Thus, we propose to create a custom conversational dataset centered on the experiences and words of a primary AAC user to fine-tune advanced language models. Additionally, we employ a Retrieval-Augmented Generation (RAG) method, drawing context from a summarized version of authored content by the AAC user. This combination ensures that responses are contextually relevant and deeply personal. Preliminary evaluations underscore its transformative potential, with automated metrics and human assessments showcasing significantly enhanced response quality."
}