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Computer Science > Computation and Language

arXiv:2209.12953 (cs)
[Submitted on 26 Sep 2022]

Title:Dialog Acts for Task-Driven Embodied Agents

Authors:Spandana Gella, Aishwarya Padmakumar, Patrick Lange, Dilek Hakkani-Tur
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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. TEACh-DA is one of 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 task success rate by up to 2 points compared to the system without dialog acts.
Comments: accepted at SIGDIAL 2022
Subjects: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2209.12953 [cs.CL]
  (or arXiv:2209.12953v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2209.12953

Submission history

From: Spandana Gella [view email]
[v1] Mon, 26 Sep 2022 18:41:28 UTC (1,922 KB)
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