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Towards Closed Feedback Loops in HRI
2014
Proceedings of the 2014 Workshop on Multimodal, Multi-Party, Real-World Human-Robot Interaction - MMRWHRI '14
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In this paper, we present a first step towards incremental processing for modeling asynchronous human-robot interactions, to allow closed feedback loops in HRI. ...
We achieve this by combining the incremental natural language processing framework InproTK with the human-robot dialog manager PaMini, which is based on generic interaction patterns. ...
If the robot dialog act contains a verbalization output, a say task is sent via RSB and further processed by the incremental speech synthesis module (iSS) of InproTK. ...
doi:10.1145/2666499.2666500
dblp:conf/icmi/CarlmeyerSW14
fatcat:6ed3qje5fjeibcydvawleu4frq
Learning the Structure of Task-Driven Human–Human Dialogs
2008
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, we use datadriven techniques to build task structures for individual dialogs, and use the dialog task structures for: dialog act classification, task/ subtask classification, task/subtask ...
With the availability of large corpora of spoken dialog, it is now possible to use data-driven techniques to build and use models of task-oriented dialogs. ...
Hollister and her team for their effort in annotating the dialogs for dialog acts and subtask structure and P. Haffner for providing them with the LLAMA ...
doi:10.1109/tasl.2008.2001102
fatcat:ig6pxvostbgtrmmvqc73z3hvmm
Learning the structure of task-driven human-human dialogs
2006
Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06
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In this paper, we use datadriven techniques to build task structures for individual dialogs, and use the dialog task structures for: dialog act classification, task/ subtask classification, task/subtask ...
With the availability of large corpora of spoken dialog, it is now possible to use data-driven techniques to build and use models of task-oriented dialogs. ...
Hollister and her team for their effort in annotating the dialogs for dialog acts and subtask structure and P. Haffner for providing them with the LLAMA ...
doi:10.3115/1220175.1220201
dblp:conf/acl/BangaloreFS06
fatcat:5sy53n6r5ffetggkfrwnfhywnm
Towards Learning Through Open-Domain Dialog
[article]
2022
arXiv
pre-print
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The development of artificial agents able to learn through dialog without domain restrictions has the potential to allow machines to learn how to perform tasks in a similar manner to humans and change ...
In this paper, we identify the modifications required for a dialog system to be able to learn from the dialog and propose generic approaches that can be used to implement those modifications. ...
Acknowledgments Eugénio Ribeiro is supported by a PhD scholarship granted by Fundac ¸ão para a Ciência e a Tecnologia (FCT), with reference SFRH/BD/148142/2019. ...
arXiv:2202.03040v1
fatcat:723ekej7hfgzrbasdwvjyoifbu
Utility-Based Generation of Referring Expressions
2012
Topics in Cognitive Science
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This paper presents two cognitive models that simulate the production of referring expressions in the iMAP task-a task-oriented dialog. ...
However, its explanatory power is limited, because it generates uniquely distinguishing referring expressions and because it considers features for inclusion in the referring expression in a fixed order ...
Thanks to Mark Steedman and in particular to Ellen Gurman Bard for comments on earlier drafts; to Dan Bothell for providing help with the ACT-R system; and to Roger van Gompel, David Reitter, and two anonymous ...
doi:10.1111/j.1756-8765.2012.01185.x
pmid:22496108
fatcat:wsbgetzpmjep3akqqoit6d4knm
Revisiting Human-Agent Communication: The Importance of Joint Co-construction and Understanding Mental States
2021
Frontiers in Psychology
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Yet, despite frequently made claims of "super-human performance" in, e.g., speech recognition or image processing, so far, no system is able to lead a half-decent coherent conversation with a human. ...
equipped with: incremental joint co-construction and mentalizing. ...
Hough and Schlangen (2016) present an extended version that models grounding and interactive repair in a task-oriented humanrobot dialog, by tracking the dialog state in two interacting state-machines ...
doi:10.3389/fpsyg.2021.580955
pmid:33833705
pmcid:PMC8021865
fatcat:nplpx3vt6rfhvgqgnx2ujr7rva
An Incremental Turn-Taking Model for Task-Oriented Dialog Systems
2019
Interspeech 2019
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In this paper, we propose a token-by-token prediction of the dialog state from incremental transcriptions of the user utterance. ...
To identify the point of maximal understanding in an ongoing utterance, we a) implement an incremental Dialog State Tracker which is updated on a token basis (iDST) b) re-label the Dialog State Tracking ...
A turn-by-turn dialog state tracker estimates the dialog state p(st) only after processing all the tokens in the user's utterance. ...
doi:10.21437/interspeech.2019-1826
dblp:conf/interspeech/ComanYM0R19
fatcat:v4wu7zxj35hgxhjqusuavuxmte
Data fusion methods in multimodal human computer dialog
2019
Virtual Reality & Intelligent Hardware
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This paper presents a review of data fusion methods in multimodal human computer dialog. ...
We first introduce the cognitive assumption of single channel processing, and then discuss its implementation methods in human computer dialog; for the task of multi-modal information fusion, serval computing ...
A type of conversational humanoid robot, which can engage in both task-oriented dialogues and non-task-oriented dialog for accurately understanding human requests or non-task-oriented dialogues to allow ...
doi:10.3724/sp.j.2096-5796.2018.0010
dblp:journals/vrih/YangT19
fatcat:jltufn3o6fd4pjzv4pnqd6wmvy
Effects of Naturalistic Variation in Goal-Oriented Dialog
[article]
2020
arXiv
pre-print
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In this work, we investigate the impact of naturalistic variation on two goal-oriented datasets: bAbI dialog task and Stanford Multi-Domain Dataset (SMD). ...
F1 on SMD and 85% in per-dialog accuracy on bAbI task) of recent state-of-the-art end-to-end neural methods such as BossNet and GLMP on both datasets. ...
, and • Publicly release improved testbeds for two datasets used extensively in goal-oriented dialog research: bAbI dialog task and SMD. ...
arXiv:2010.02260v1
fatcat:qsharrsbbnczlcj2wvwyptecbe
Robust Conversational AI with Grounded Text Generation
[article]
2020
arXiv
pre-print
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responses grounded in dialog belief state and real-world knowledge for task completion. ...
The primary results reported on task-oriented dialog benchmarks are very promising, demonstrating the big potential of this approach. ...
GTG for task-oriented dialog Consider a multi-turn task-oriented dialog, as illustrated in Figure 9 . At each turn, GTG generates a natural language response r in the following steps. ...
arXiv:2009.03457v1
fatcat:2462mlxn7fg3zgw5mwknoqngaa
Incremental partition recombination for efficient tracking of multiple dialog states
2010
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
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To retain tractability, past work has suggested tracking dialog states in groups called partitions. ...
For spoken dialog systems, tracking a distribution over multiple dialog states has been shown to add robustness to speech recognition errors. ...
In practice there are too many dialog states to process in real time, so researchers have suggested maintaining a distribution over partitions of dialog states [3, 4] . ...
doi:10.1109/icassp.2010.5494939
dblp:conf/icassp/Williams10a
fatcat:5sifovxag5bnhnsrz5f2pl4auy
Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation
[article]
2019
arXiv
pre-print
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Reinforcement learning (RL) is an effective approach to learn an optimal dialog policy for task-oriented visual dialog systems. ...
We evaluate our framework on the GuessWhich task and the framework achieves the state-of-the-art performance in both task completion and dialog quality. ...
Acknowledgments This work was supported in part by Cisco. ...
arXiv:1909.05365v2
fatcat:avidxwp3nfavjmlkrwizo6lxva
Incremental Coordination: Attention-Centric Speech Production in a Physically Situated Conversational Agent
2015
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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We present an implementation and deployment of the model in a physically situated dialog system and discuss lessons learned. ...
We introduce a model that considers the demands and availability of listeners' attention at the onset and throughout the production of system utterances, and that incrementally coordinates speech synthesis ...
Most previous work on incremental processing in dialog has focused on the acoustic channel, including efforts on recognizing, generating, and synthesizing language incrementally. ...
doi:10.18653/v1/w15-4652
dblp:conf/sigdial/YuBH15
fatcat:eez74ipf2neuzfgwzm5xavmwbe
Description of Experiments
[chapter]
1974
Monkeys As Perceivers
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The emphasis of the analysis presented in this paper addresses (1) the structure of dialog in relation to the process of assembly, which we found exemplified a relationship between dialog and task, (2 ...
Incremental focusing works to establish a group of one. Incremental focusing strategies are usually in the form of utterances which exhibit a top-down approach. ...
doi:10.1016/b978-0-12-534003-8.50017-7
fatcat:szsz3urvefegvjdg7nbakarbiq
Report from the NSF Future Directions Workshop, Toward User-Oriented Agents: Research Directions and Challenges
[article]
2020
arXiv
pre-print
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In the breakout sessions that followed, the participants defined the main research areas within the domain of intelligent agents and they discussed the major future directions that the research in each ...
It took place in Pittsburgh Pennsylvania on October 24 and 25, 2019 and was sponsored by National Science Foundation Grant Number IIS-1934222. ...
The evaluation of traditional task-oriented dialog systems has focused on objective measures of the quality of system components, task success, and user satisfaction as measured in post-use questionnaires ...
arXiv:2006.06026v1
fatcat:h2hbe4cr6vajvca4oqkfrmf5oa
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