@inproceedings{h-kumar-etal-2022-cue,
title = "Cue-bot: A Conversational Agent for Assistive Technology",
author = "H Kumar, Shachi and
Su, Hsuan and
Manuvinakurike, Ramesh and
Pinaroc, Maximilian C. and
Prasad, Sai and
Sahay, Saurav and
Nachman, Lama",
editor = "Basile, Valerio and
Kozareva, Zornitsa and
Stajner, Sanja",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-demo.19",
doi = "10.18653/v1/2022.acl-demo.19",
pages = "196--203",
abstract = "Intelligent conversational assistants have become an integral part of our lives for performing simple tasks. However, such agents, for example, Google bots, Alexa and others are yet to have any social impact on minority population, for example, for people with neurological disorders and people with speech, language and social communication disorders, sometimes with locked-in states where speaking or typing is a challenge. Language model technologies can be very powerful tools in enabling these users to carry out daily communication and social interactions. In this work, we present a system that users with varied levels of disabilties can use to interact with the world, supported by eye-tracking, mouse controls and an intelligent agent Cue-bot, that can represent the user in a conversation. The agent provides relevant controllable {`}cues{'} to generate desirable responses quickly for an ongoing dialog context. In the context of usage of such systems for people with degenerative disorders, we present automatic and human evaluation of our cue/keyword predictor and the controllable dialog system and show that our models perform significantly better than models without control and can also reduce user effort (fewer keystrokes) and speed up communication (typing time) significantly.",
}
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<abstract>Intelligent conversational assistants have become an integral part of our lives for performing simple tasks. However, such agents, for example, Google bots, Alexa and others are yet to have any social impact on minority population, for example, for people with neurological disorders and people with speech, language and social communication disorders, sometimes with locked-in states where speaking or typing is a challenge. Language model technologies can be very powerful tools in enabling these users to carry out daily communication and social interactions. In this work, we present a system that users with varied levels of disabilties can use to interact with the world, supported by eye-tracking, mouse controls and an intelligent agent Cue-bot, that can represent the user in a conversation. The agent provides relevant controllable ‘cues’ to generate desirable responses quickly for an ongoing dialog context. In the context of usage of such systems for people with degenerative disorders, we present automatic and human evaluation of our cue/keyword predictor and the controllable dialog system and show that our models perform significantly better than models without control and can also reduce user effort (fewer keystrokes) and speed up communication (typing time) significantly.</abstract>
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%0 Conference Proceedings
%T Cue-bot: A Conversational Agent for Assistive Technology
%A H Kumar, Shachi
%A Su, Hsuan
%A Manuvinakurike, Ramesh
%A Pinaroc, Maximilian C.
%A Prasad, Sai
%A Sahay, Saurav
%A Nachman, Lama
%Y Basile, Valerio
%Y Kozareva, Zornitsa
%Y Stajner, Sanja
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F h-kumar-etal-2022-cue
%X Intelligent conversational assistants have become an integral part of our lives for performing simple tasks. However, such agents, for example, Google bots, Alexa and others are yet to have any social impact on minority population, for example, for people with neurological disorders and people with speech, language and social communication disorders, sometimes with locked-in states where speaking or typing is a challenge. Language model technologies can be very powerful tools in enabling these users to carry out daily communication and social interactions. In this work, we present a system that users with varied levels of disabilties can use to interact with the world, supported by eye-tracking, mouse controls and an intelligent agent Cue-bot, that can represent the user in a conversation. The agent provides relevant controllable ‘cues’ to generate desirable responses quickly for an ongoing dialog context. In the context of usage of such systems for people with degenerative disorders, we present automatic and human evaluation of our cue/keyword predictor and the controllable dialog system and show that our models perform significantly better than models without control and can also reduce user effort (fewer keystrokes) and speed up communication (typing time) significantly.
%R 10.18653/v1/2022.acl-demo.19
%U https://aclanthology.org/2022.acl-demo.19
%U https://doi.org/10.18653/v1/2022.acl-demo.19
%P 196-203
Markdown (Informal)
[Cue-bot: A Conversational Agent for Assistive Technology](https://aclanthology.org/2022.acl-demo.19) (H Kumar et al., ACL 2022)
ACL
- Shachi H Kumar, Hsuan Su, Ramesh Manuvinakurike, Maximilian C. Pinaroc, Sai Prasad, Saurav Sahay, and Lama Nachman. 2022. Cue-bot: A Conversational Agent for Assistive Technology. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 196–203, Dublin, Ireland. Association for Computational Linguistics.