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        <title>Dialogue Act Classification for Augmentative and Alternative Communication</title>
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        <namePart type="given">Margaret</namePart>
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            <title>Proceedings of the 1st Workshop on NLP for Positive Impact</title>
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            <namePart type="given">Anjalie</namePart>
            <namePart type="family">Field</namePart>
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            <namePart type="given">Shrimai</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.nlp4posimpact-1.12</identifier>
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%0 Conference Proceedings
%T Dialogue Act Classification for Augmentative and Alternative Communication
%A Perkoff, E. Margaret
%Y Field, Anjalie
%Y Prabhumoye, Shrimai
%Y Sap, Maarten
%Y Jin, Zhijing
%Y Zhao, Jieyu
%Y Brockett, Chris
%S Proceedings of the 1st Workshop on NLP for Positive Impact
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F perkoff-2021-dialogue
%X 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.
%R 10.18653/v1/2021.nlp4posimpact-1.12
%U https://aclanthology.org/2021.nlp4posimpact-1.12
%U https://doi.org/10.18653/v1/2021.nlp4posimpact-1.12
%P 107-114

Markdown (Informal)

[Dialogue Act Classification for Augmentative and Alternative Communication](https://aclanthology.org/2021.nlp4posimpact-1.12) (Perkoff, NLP4PI 2021)

• Dialogue Act Classification for Augmentative and Alternative Communication (Perkoff, NLP4PI 2021)

ACL

• E. Margaret Perkoff. 2021. Dialogue Act Classification for Augmentative and Alternative Communication. In Proceedings of the 1st Workshop on NLP for Positive Impact, pages 107–114, Online. Association for Computational Linguistics.