@inproceedings{perkoff-2021-dialogue,
    title = "Dialogue Act Classification for Augmentative and Alternative Communication",
    author = "Perkoff, E. Margaret",
    editor = "Field, Anjalie  and
      Prabhumoye, Shrimai  and
      Sap, Maarten  and
      Jin, Zhijing  and
      Zhao, Jieyu  and
      Brockett, Chris",
    booktitle = "Proceedings of the 1st Workshop on NLP for Positive Impact",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.nlp4posimpact-1.12/",
    doi = "10.18653/v1/2021.nlp4posimpact-1.12",
    pages = "107--114",
    abstract = "Augmentative and Alternative Communication (AAC) devices and applications are intended to make it easier for individuals with complex communication needs to participate in conversations. However, these devices have low adoption and retention rates. We review prior work with text recommendation systems that have not been successful in mitigating these problems. To address these gaps, we propose applying Dialogue Act classification to AAC conversations. We evaluated the performance of a state of the art model on a limited AAC dataset that was trained on both AAC and non-AAC datasets. The one trained on AAC (accuracy = 38.6{\%}) achieved better performance than that trained on a non-AAC corpus (accuracy = 34.1{\%}). These results reflect the need to incorporate representative datasets in later experiments. We discuss the need to collect more labeled AAC datasets and propose areas of future work."
}