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    <abstract>Conversational assistants are ubiquitous among the general population, however, these systems have not had an impact on people with disabilities, or speech and language disorders, for whom basic day-to-day communication and social interaction is a huge struggle. Language model technology can play a huge role in empowering these users and help them interact with others with less effort via interaction support. To enable this population, we build a system that can represent them in a social conversation and generate responses that can be controlled by the users using cues/keywords. We build models that can speed up this communication by suggesting relevant cues in the dialog response context. We also introduce a keyword-loss to lexically constrain the model response output. We present automatic and human evaluation of our cue/keyword predictor and the controllable dialog system to show that our models perform significantly better than models without control. Our evaluation and user study shows that keyword-control on end-to-end response generation models is powerful and can enable and empower users with degenerative disorders to carry out their day-to-day communication.</abstract>
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%0 Conference Proceedings
%T CueBot: Cue-Controlled Response Generation for Assistive Interaction Usages
%A H. Kumar, Shachi
%A Su, Hsuan
%A Manuvinakurike, Ramesh
%A Pinaroc, Max
%A Prasad, Sai
%A Sahay, Saurav
%A Nachman, Lama
%Y Ebling, Sarah
%Y Prud’hommeaux, Emily
%Y Vaidyanathan, Preethi
%S Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F h-kumar-etal-2022-cuebot
%X Conversational assistants are ubiquitous among the general population, however, these systems have not had an impact on people with disabilities, or speech and language disorders, for whom basic day-to-day communication and social interaction is a huge struggle. Language model technology can play a huge role in empowering these users and help them interact with others with less effort via interaction support. To enable this population, we build a system that can represent them in a social conversation and generate responses that can be controlled by the users using cues/keywords. We build models that can speed up this communication by suggesting relevant cues in the dialog response context. We also introduce a keyword-loss to lexically constrain the model response output. We present automatic and human evaluation of our cue/keyword predictor and the controllable dialog system to show that our models perform significantly better than models without control. Our evaluation and user study shows that keyword-control on end-to-end response generation models is powerful and can enable and empower users with degenerative disorders to carry out their day-to-day communication.
%R 10.18653/v1/2022.slpat-1.9
%U https://aclanthology.org/2022.slpat-1.9
%U https://doi.org/10.18653/v1/2022.slpat-1.9
%P 66-79

Markdown (Informal)

[CueBot: Cue-Controlled Response Generation for Assistive Interaction Usages](https://aclanthology.org/2022.slpat-1.9) (H. Kumar et al., SLPAT 2022)

• CueBot: Cue-Controlled Response Generation for Assistive Interaction Usages (H. Kumar et al., SLPAT 2022)

ACL

• Shachi H. Kumar, Hsuan Su, Ramesh Manuvinakurike, Max Pinaroc, Sai Prasad, Saurav Sahay, and Lama Nachman. 2022. CueBot: Cue-Controlled Response Generation for Assistive Interaction Usages. In Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022), pages 66–79, Dublin, Ireland. Association for Computational Linguistics.