@inproceedings{gella-etal-2022-dialog,
title = "Dialog Acts for Task Driven Embodied Agents",
author = "Gella, Spandana and
Padmakumar, Aishwarya and
Lange, Patrick and
Hakkani-Tur, Dilek",
editor = "Lemon, Oliver and
Hakkani-Tur, Dilek and
Li, Junyi Jessy and
Ashrafzadeh, Arash and
Garcia, Daniel Hern{\'a}ndez and
Alikhani, Malihe and
Vandyke, David and
Du{\v{s}}ek, Ond{\v{r}}ej",
booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2022",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigdial-1.13",
doi = "10.18653/v1/2022.sigdial-1.13",
pages = "111--123",
abstract = "Embodied agents need to be able to interact in natural language {--} understanding task descriptions and asking appropriate follow up questions to obtain necessary information to be effective at successfully accomplishing tasks for a wide range of users. In this work, we propose a set of dialog acts for modelling such dialogs and annotate the TEACh dataset that includes over 3,000 situated, task oriented conversations (consisting of 39.5k utterances in total) with dialog acts. To our knowledge,TEACh-DA is the first large scale dataset of dialog act annotations for embodied task completion. Furthermore, we demonstrate the use of this annotated dataset in training models for tagging the dialog acts of a given utterance, predicting the dialog act of the next response given a dialog history, and use the dialog acts to guide agent{'}s non-dialog behaviour. In particular, our experiments on the TEACh Execution from Dialog History task where the model predicts the sequence of low level actions to be executed in the environment for embodied task completion, demonstrate that dialog acts can improve end performance by up to 2 points compared to the system without dialog acts.",
}
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<abstract>Embodied agents need to be able to interact in natural language – understanding task descriptions and asking appropriate follow up questions to obtain necessary information to be effective at successfully accomplishing tasks for a wide range of users. In this work, we propose a set of dialog acts for modelling such dialogs and annotate the TEACh dataset that includes over 3,000 situated, task oriented conversations (consisting of 39.5k utterances in total) with dialog acts. To our knowledge,TEACh-DA is the first large scale dataset of dialog act annotations for embodied task completion. Furthermore, we demonstrate the use of this annotated dataset in training models for tagging the dialog acts of a given utterance, predicting the dialog act of the next response given a dialog history, and use the dialog acts to guide agent’s non-dialog behaviour. In particular, our experiments on the TEACh Execution from Dialog History task where the model predicts the sequence of low level actions to be executed in the environment for embodied task completion, demonstrate that dialog acts can improve end performance by up to 2 points compared to the system without dialog acts.</abstract>
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%0 Conference Proceedings
%T Dialog Acts for Task Driven Embodied Agents
%A Gella, Spandana
%A Padmakumar, Aishwarya
%A Lange, Patrick
%A Hakkani-Tur, Dilek
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F gella-etal-2022-dialog
%X Embodied agents need to be able to interact in natural language – understanding task descriptions and asking appropriate follow up questions to obtain necessary information to be effective at successfully accomplishing tasks for a wide range of users. In this work, we propose a set of dialog acts for modelling such dialogs and annotate the TEACh dataset that includes over 3,000 situated, task oriented conversations (consisting of 39.5k utterances in total) with dialog acts. To our knowledge,TEACh-DA is the first large scale dataset of dialog act annotations for embodied task completion. Furthermore, we demonstrate the use of this annotated dataset in training models for tagging the dialog acts of a given utterance, predicting the dialog act of the next response given a dialog history, and use the dialog acts to guide agent’s non-dialog behaviour. In particular, our experiments on the TEACh Execution from Dialog History task where the model predicts the sequence of low level actions to be executed in the environment for embodied task completion, demonstrate that dialog acts can improve end performance by up to 2 points compared to the system without dialog acts.
%R 10.18653/v1/2022.sigdial-1.13
%U https://aclanthology.org/2022.sigdial-1.13
%U https://doi.org/10.18653/v1/2022.sigdial-1.13
%P 111-123
Markdown (Informal)
[Dialog Acts for Task Driven Embodied Agents](https://aclanthology.org/2022.sigdial-1.13) (Gella et al., SIGDIAL 2022)
ACL
- Spandana Gella, Aishwarya Padmakumar, Patrick Lange, and Dilek Hakkani-Tur. 2022. Dialog Acts for Task Driven Embodied Agents. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 111–123, Edinburgh, UK. Association for Computational Linguistics.