Publications
Search
1.
Speggiorin, Alessandro; Dalton, Jeffrey; Leuski, Anton
TaskMAD: A Platform for Multimodal Task-Centric Knowledge-Grounded Conversational Experimentation Proceedings Article
In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3240–3244, ACM, Madrid Spain, 2022, ISBN: 978-1-4503-8732-3.
@inproceedings{speggiorin_taskmad_2022,
title = {TaskMAD: A Platform for Multimodal Task-Centric Knowledge-Grounded Conversational Experimentation},
author = {Alessandro Speggiorin and Jeffrey Dalton and Anton Leuski},
url = {https://dl.acm.org/doi/10.1145/3477495.3531679},
doi = {10.1145/3477495.3531679},
isbn = {978-1-4503-8732-3},
year = {2022},
date = {2022-07-01},
urldate = {2022-09-22},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {3240–3244},
publisher = {ACM},
address = {Madrid Spain},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2.
Marge, Matthew; Espy-Wilson, Carol; Ward, Nigel G.; Alwan, Abeer; Artzi, Yoav; Bansal, Mohit; Blankenship, Gil; Chai, Joyce; Daumé, Hal; Dey, Debadeepta; Harper, Mary; Howard, Thomas; Kennington, Casey; Kruijff-Korbayová, Ivana; Manocha, Dinesh; Matuszek, Cynthia; Mead, Ross; Mooney, Raymond; Moore, Roger K.; Ostendorf, Mari; Pon-Barry, Heather; Rudnicky, Alexander I.; Scheutz, Matthias; Amant, Robert St.; Sun, Tong; Tellex, Stefanie; Traum, David; Yu, Zhou
Spoken language interaction with robots: Recommendations for future research Journal Article
In: Computer Speech & Language, vol. 71, pp. 101255, 2022, ISSN: 08852308.
@article{marge_spoken_2022,
title = {Spoken language interaction with robots: Recommendations for future research},
author = {Matthew Marge and Carol Espy-Wilson and Nigel G. Ward and Abeer Alwan and Yoav Artzi and Mohit Bansal and Gil Blankenship and Joyce Chai and Hal Daumé and Debadeepta Dey and Mary Harper and Thomas Howard and Casey Kennington and Ivana Kruijff-Korbayová and Dinesh Manocha and Cynthia Matuszek and Ross Mead and Raymond Mooney and Roger K. Moore and Mari Ostendorf and Heather Pon-Barry and Alexander I. Rudnicky and Matthias Scheutz and Robert St. Amant and Tong Sun and Stefanie Tellex and David Traum and Zhou Yu},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0885230821000620},
doi = {10.1016/j.csl.2021.101255},
issn = {08852308},
year = {2022},
date = {2022-01-01},
urldate = {2022-09-23},
journal = {Computer Speech & Language},
volume = {71},
pages = {101255},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3.
Bonial, Claire; Abrams, Mitchell; Baker, Anthony L.; Hudson, Taylor; Lukin, Stephanie; Traum, David; Voss, Clare
Context is key: Annotating situated dialogue relations in multi-floor dialogue Proceedings Article
In: 2021.
@inproceedings{bonial_context_2021,
title = {Context is key: Annotating situated dialogue relations in multi-floor dialogue},
author = {Claire Bonial and Mitchell Abrams and Anthony L. Baker and Taylor Hudson and Stephanie Lukin and David Traum and Clare Voss},
url = {http://semdial.org/anthology/papers/Z/Z21/Z21-3006/},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-23},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
4.
Chaffey, Patricia; Traum, David
Identity models for role-play dialogue characters Proceedings Article
In: 2021.
@inproceedings{chaffey_identity_2021,
title = {Identity models for role-play dialogue characters},
author = {Patricia Chaffey and David Traum},
url = {http://semdial.org/anthology/papers/Z/Z21/Z21-4022/},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-23},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
5.
Lugrin, Birgit; Pelachaud, Catherine; Traum, David (Ed.)
1, ACM, New York, NY, USA, 2021, ISBN: 978-1-4503-8720-0.
@book{lugrin_handbook_2021,
title = {The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 1: Methods, Behavior, Cognition},
editor = {Birgit Lugrin and Catherine Pelachaud and David Traum},
url = {https://dl.acm.org/doi/book/10.1145/3477322},
doi = {10.1145/3477322},
isbn = {978-1-4503-8720-0},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-23},
publisher = {ACM},
address = {New York, NY, USA},
edition = {1},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
6.
Bonial, Claire; Abrams, Mitchell; Traum, David; Voss, Clare
Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains Proceedings Article
In: Proceedings of the 14th International Conference on Computational Semantics (IWCS), pp. 173–183, Association for Computational Linguistics, Groningen, The Netherlands (online), 2021.
@inproceedings{bonial_builder_2021,
title = {Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains},
author = {Claire Bonial and Mitchell Abrams and David Traum and Clare Voss},
url = {https://aclanthology.org/2021.iwcs-1.17},
year = {2021},
date = {2021-06-01},
urldate = {2022-09-23},
booktitle = {Proceedings of the 14th International Conference on Computational Semantics (IWCS)},
pages = {173–183},
publisher = {Association for Computational Linguistics},
address = {Groningen, The Netherlands (online)},
abstract = {We adopt, evaluate, and improve upon a two-step natural language understanding (NLU) pipeline that incrementally tames the variation of unconstrained natural language input and maps to executable robot behaviors. The pipeline first leverages Abstract Meaning Representation (AMR) parsing to capture the propositional content of the utterance, and second converts this into “Dialogue-AMR,” which augments standard AMR with information on tense, aspect, and speech acts. Several alternative approaches and training datasets are evaluated for both steps and corresponding components of the pipeline, some of which outperform the original. We extend the Dialogue-AMR annotation schema to cover a different collaborative instruction domain and evaluate on both domains. With very little training data, we achieve promising performance in the new domain, demonstrating the scalability of this approach.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We adopt, evaluate, and improve upon a two-step natural language understanding (NLU) pipeline that incrementally tames the variation of unconstrained natural language input and maps to executable robot behaviors. The pipeline first leverages Abstract Meaning Representation (AMR) parsing to capture the propositional content of the utterance, and second converts this into “Dialogue-AMR,” which augments standard AMR with information on tense, aspect, and speech acts. Several alternative approaches and training datasets are evaluated for both steps and corresponding components of the pipeline, some of which outperform the original. We extend the Dialogue-AMR annotation schema to cover a different collaborative instruction domain and evaluate on both domains. With very little training data, we achieve promising performance in the new domain, demonstrating the scalability of this approach.
7.
Gordon, Carla; Georgila, Kallirroi; Yanov, Volodymyr; Traum, David
Towards Personalization of Spoken Dialogue System Communication Strategies Book Section
In: D'Haro, Luis Fernando; Callejas, Zoraida; Nakamura, Satoshi (Ed.): Conversational Dialogue Systems for the Next Decade, vol. 704, pp. 145–160, Springer Singapore, Singapore, 2021, ISBN: 9789811583940 9789811583957, (Series Title: Lecture Notes in Electrical Engineering).
@incollection{dharo_towards_2021,
title = {Towards Personalization of Spoken Dialogue System Communication Strategies},
author = {Carla Gordon and Kallirroi Georgila and Volodymyr Yanov and David Traum},
editor = {Luis Fernando D'Haro and Zoraida Callejas and Satoshi Nakamura},
url = {http://link.springer.com/10.1007/978-981-15-8395-7_11},
doi = {10.1007/978-981-15-8395-7_11},
isbn = {9789811583940 9789811583957},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
booktitle = {Conversational Dialogue Systems for the Next Decade},
volume = {704},
pages = {145--160},
publisher = {Springer Singapore},
address = {Singapore},
abstract = {This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.},
note = {Series Title: Lecture Notes in Electrical Engineering},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.
8.
Gervits, Felix; Leuski, Anton; Bonial, Claire; Gordon, Carla; Traum, David
A Classification-Based Approach to Automating Human-Robot Dialogue Book Section
In: Marchi, Erik; Siniscalchi, Sabato Marco; Cumani, Sandro; Salerno, Valerio Mario; Li, Haizhou (Ed.): Increasing Naturalness and Flexibility in Spoken Dialogue Interaction: 10th International Workshop on Spoken Dialogue Systems, pp. 115–127, Springer, Singapore, 2021, ISBN: 9789811593239.
@incollection{gervits_classification-based_2021,
title = {A Classification-Based Approach to Automating Human-Robot Dialogue},
author = {Felix Gervits and Anton Leuski and Claire Bonial and Carla Gordon and David Traum},
editor = {Erik Marchi and Sabato Marco Siniscalchi and Sandro Cumani and Valerio Mario Salerno and Haizhou Li},
url = {https://doi.org/10.1007/978-981-15-9323-9_10},
doi = {10.1007/978-981-15-9323-9_10},
isbn = {9789811593239},
year = {2021},
date = {2021-01-01},
urldate = {2022-09-23},
booktitle = {Increasing Naturalness and Flexibility in Spoken Dialogue Interaction: 10th International Workshop on Spoken Dialogue Systems},
pages = {115–127},
publisher = {Springer},
address = {Singapore},
series = {Lecture Notes in Electrical Engineering},
abstract = {We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multi-floor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multi-floor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.
9.
He, Zihao; Tavabi, Leili; Lerman, Kristina; Soleymani, Mohammad
Speaker Turn Modeling for Dialogue Act Classification Proceedings Article
In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 2150–2157, Association for Computational Linguistics, Punta Cana, Dominican Republic, 2021.
@inproceedings{he_speaker_2021,
title = {Speaker Turn Modeling for Dialogue Act Classification},
author = {Zihao He and Leili Tavabi and Kristina Lerman and Mohammad Soleymani},
url = {https://aclanthology.org/2021.findings-emnlp.185},
doi = {10.18653/v1/2021.findings-emnlp.185},
year = {2021},
date = {2021-01-01},
urldate = {2022-09-23},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2021},
pages = {2150–2157},
publisher = {Association for Computational Linguistics},
address = {Punta Cana, Dominican Republic},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
10.
Kawano, Seiya; Yoshino, Koichiro; Traum, David; Nakamura, Satoshi
Dialogue Structure Parsing on Multi-Floor Dialogue Based on Multi-Task Learning Proceedings Article
In: 1st RobotDial Workshop on Dialogue Models for Human-Robot Interaction, pp. 21–29, ISCA, 2021.
@inproceedings{kawano_dialogue_2021,
title = {Dialogue Structure Parsing on Multi-Floor Dialogue Based on Multi-Task Learning},
author = {Seiya Kawano and Koichiro Yoshino and David Traum and Satoshi Nakamura},
url = {http://www.isca-speech.org/archive/RobotDial_2021/abstracts/4.html},
doi = {10.21437/RobotDial.2021-4},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
booktitle = {1st RobotDial Workshop on Dialogue Models for Human-Robot Interaction},
pages = {21–29},
publisher = {ISCA},
abstract = {A multi-floor dialogue consists of multiple sets of dialogue participants, each conversing within their own floor, but also at least one multicommunicating member who is a participant of multiple floors and coordinating each to achieve a shared dialogue goal. The structure of such dialogues can be complex, involving intentional structure and relations that are within or across floors. In this study, we propose a neural dialogue structure parser based on multi-task learning and an attention mechanism on multi-floor dialogues in a collaborative robot navigation domain. Our experimental results show that our proposed model improved the dialogue structure parsing performance more than those of single models, which are trained on each dialogue structure parsing task in multi-floor dialogues.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
A multi-floor dialogue consists of multiple sets of dialogue participants, each conversing within their own floor, but also at least one multicommunicating member who is a participant of multiple floors and coordinating each to achieve a shared dialogue goal. The structure of such dialogues can be complex, involving intentional structure and relations that are within or across floors. In this study, we propose a neural dialogue structure parser based on multi-task learning and an attention mechanism on multi-floor dialogues in a collaborative robot navigation domain. Our experimental results show that our proposed model improved the dialogue structure parsing performance more than those of single models, which are trained on each dialogue structure parsing task in multi-floor dialogues.
11.
Gordon, Carla; Georgila, Kallirroi; Yanov, Volodymyr; Traum, David
Towards Personalization of Spoken Dialogue System Communication Strategies Book Section
In: Conversational Dialogue Systems for the Next Decade, vol. 704, pp. 145–160, Springer Singapore, Singapore, 2020, ISBN: 9789811583940 9789811583957.
@incollection{gordon_towards_2020,
title = {Towards Personalization of Spoken Dialogue System Communication Strategies},
author = {Carla Gordon and Kallirroi Georgila and Volodymyr Yanov and David Traum},
url = {http://link.springer.com/10.1007/978-981-15-8395-7_11},
isbn = {9789811583940 9789811583957},
year = {2020},
date = {2020-09-01},
booktitle = {Conversational Dialogue Systems for the Next Decade},
volume = {704},
pages = {145–160},
publisher = {Springer Singapore},
address = {Singapore},
abstract = {This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.
12.
Gervits, Felix; Leuski, Anton; Bonial, Claire; Gordon, Carla; Traum, David
A Classification-Based Approach to Automating Human-Robot Dialogue Journal Article
In: pp. 13, 0000.
@article{gervits_classication-based_nodate,
title = {A Classification-Based Approach to Automating Human-Robot Dialogue},
author = {Felix Gervits and Anton Leuski and Claire Bonial and Carla Gordon and David Traum},
url = {https://link.springer.com/chapter/10.1007/978-981-15-9323-9_10},
doi = {https://doi.org/10.1007/978-981-15-9323-9_10},
pages = {13},
abstract = {We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multifloor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multifloor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.
Filter
2022
Speggiorin, Alessandro; Dalton, Jeffrey; Leuski, Anton
TaskMAD: A Platform for Multimodal Task-Centric Knowledge-Grounded Conversational Experimentation Proceedings Article
In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3240–3244, ACM, Madrid Spain, 2022, ISBN: 978-1-4503-8732-3.
Links | BibTeX | Tags: Dialogue, DTIC, UARC
@inproceedings{speggiorin_taskmad_2022,
title = {TaskMAD: A Platform for Multimodal Task-Centric Knowledge-Grounded Conversational Experimentation},
author = {Alessandro Speggiorin and Jeffrey Dalton and Anton Leuski},
url = {https://dl.acm.org/doi/10.1145/3477495.3531679},
doi = {10.1145/3477495.3531679},
isbn = {978-1-4503-8732-3},
year = {2022},
date = {2022-07-01},
urldate = {2022-09-22},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {3240–3244},
publisher = {ACM},
address = {Madrid Spain},
keywords = {Dialogue, DTIC, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Marge, Matthew; Espy-Wilson, Carol; Ward, Nigel G.; Alwan, Abeer; Artzi, Yoav; Bansal, Mohit; Blankenship, Gil; Chai, Joyce; Daumé, Hal; Dey, Debadeepta; Harper, Mary; Howard, Thomas; Kennington, Casey; Kruijff-Korbayová, Ivana; Manocha, Dinesh; Matuszek, Cynthia; Mead, Ross; Mooney, Raymond; Moore, Roger K.; Ostendorf, Mari; Pon-Barry, Heather; Rudnicky, Alexander I.; Scheutz, Matthias; Amant, Robert St.; Sun, Tong; Tellex, Stefanie; Traum, David; Yu, Zhou
Spoken language interaction with robots: Recommendations for future research Journal Article
In: Computer Speech & Language, vol. 71, pp. 101255, 2022, ISSN: 08852308.
Links | BibTeX | Tags: ARL, Dialogue
@article{marge_spoken_2022,
title = {Spoken language interaction with robots: Recommendations for future research},
author = {Matthew Marge and Carol Espy-Wilson and Nigel G. Ward and Abeer Alwan and Yoav Artzi and Mohit Bansal and Gil Blankenship and Joyce Chai and Hal Daumé and Debadeepta Dey and Mary Harper and Thomas Howard and Casey Kennington and Ivana Kruijff-Korbayová and Dinesh Manocha and Cynthia Matuszek and Ross Mead and Raymond Mooney and Roger K. Moore and Mari Ostendorf and Heather Pon-Barry and Alexander I. Rudnicky and Matthias Scheutz and Robert St. Amant and Tong Sun and Stefanie Tellex and David Traum and Zhou Yu},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0885230821000620},
doi = {10.1016/j.csl.2021.101255},
issn = {08852308},
year = {2022},
date = {2022-01-01},
urldate = {2022-09-23},
journal = {Computer Speech & Language},
volume = {71},
pages = {101255},
keywords = {ARL, Dialogue},
pubstate = {published},
tppubtype = {article}
}
2021
Bonial, Claire; Abrams, Mitchell; Baker, Anthony L.; Hudson, Taylor; Lukin, Stephanie; Traum, David; Voss, Clare
Context is key: Annotating situated dialogue relations in multi-floor dialogue Proceedings Article
In: 2021.
Links | BibTeX | Tags: Dialogue, DTIC
@inproceedings{bonial_context_2021,
title = {Context is key: Annotating situated dialogue relations in multi-floor dialogue},
author = {Claire Bonial and Mitchell Abrams and Anthony L. Baker and Taylor Hudson and Stephanie Lukin and David Traum and Clare Voss},
url = {http://semdial.org/anthology/papers/Z/Z21/Z21-3006/},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-23},
keywords = {Dialogue, DTIC},
pubstate = {published},
tppubtype = {inproceedings}
}
Chaffey, Patricia; Traum, David
Identity models for role-play dialogue characters Proceedings Article
In: 2021.
Links | BibTeX | Tags: Dialogue, DTIC, UARC
@inproceedings{chaffey_identity_2021,
title = {Identity models for role-play dialogue characters},
author = {Patricia Chaffey and David Traum},
url = {http://semdial.org/anthology/papers/Z/Z21/Z21-4022/},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-23},
keywords = {Dialogue, DTIC, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Lugrin, Birgit; Pelachaud, Catherine; Traum, David (Ed.)
1, ACM, New York, NY, USA, 2021, ISBN: 978-1-4503-8720-0.
Links | BibTeX | Tags: Dialogue, Virtual Humans
@book{lugrin_handbook_2021,
title = {The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 1: Methods, Behavior, Cognition},
editor = {Birgit Lugrin and Catherine Pelachaud and David Traum},
url = {https://dl.acm.org/doi/book/10.1145/3477322},
doi = {10.1145/3477322},
isbn = {978-1-4503-8720-0},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-23},
publisher = {ACM},
address = {New York, NY, USA},
edition = {1},
keywords = {Dialogue, Virtual Humans},
pubstate = {published},
tppubtype = {book}
}
Bonial, Claire; Abrams, Mitchell; Traum, David; Voss, Clare
Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains Proceedings Article
In: Proceedings of the 14th International Conference on Computational Semantics (IWCS), pp. 173–183, Association for Computational Linguistics, Groningen, The Netherlands (online), 2021.
Abstract | Links | BibTeX | Tags: Dialogue, DTIC
@inproceedings{bonial_builder_2021,
title = {Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains},
author = {Claire Bonial and Mitchell Abrams and David Traum and Clare Voss},
url = {https://aclanthology.org/2021.iwcs-1.17},
year = {2021},
date = {2021-06-01},
urldate = {2022-09-23},
booktitle = {Proceedings of the 14th International Conference on Computational Semantics (IWCS)},
pages = {173–183},
publisher = {Association for Computational Linguistics},
address = {Groningen, The Netherlands (online)},
abstract = {We adopt, evaluate, and improve upon a two-step natural language understanding (NLU) pipeline that incrementally tames the variation of unconstrained natural language input and maps to executable robot behaviors. The pipeline first leverages Abstract Meaning Representation (AMR) parsing to capture the propositional content of the utterance, and second converts this into “Dialogue-AMR,” which augments standard AMR with information on tense, aspect, and speech acts. Several alternative approaches and training datasets are evaluated for both steps and corresponding components of the pipeline, some of which outperform the original. We extend the Dialogue-AMR annotation schema to cover a different collaborative instruction domain and evaluate on both domains. With very little training data, we achieve promising performance in the new domain, demonstrating the scalability of this approach.},
keywords = {Dialogue, DTIC},
pubstate = {published},
tppubtype = {inproceedings}
}
We adopt, evaluate, and improve upon a two-step natural language understanding (NLU) pipeline that incrementally tames the variation of unconstrained natural language input and maps to executable robot behaviors. The pipeline first leverages Abstract Meaning Representation (AMR) parsing to capture the propositional content of the utterance, and second converts this into “Dialogue-AMR,” which augments standard AMR with information on tense, aspect, and speech acts. Several alternative approaches and training datasets are evaluated for both steps and corresponding components of the pipeline, some of which outperform the original. We extend the Dialogue-AMR annotation schema to cover a different collaborative instruction domain and evaluate on both domains. With very little training data, we achieve promising performance in the new domain, demonstrating the scalability of this approach.
Gordon, Carla; Georgila, Kallirroi; Yanov, Volodymyr; Traum, David
Towards Personalization of Spoken Dialogue System Communication Strategies Book Section
In: D'Haro, Luis Fernando; Callejas, Zoraida; Nakamura, Satoshi (Ed.): Conversational Dialogue Systems for the Next Decade, vol. 704, pp. 145–160, Springer Singapore, Singapore, 2021, ISBN: 9789811583940 9789811583957, (Series Title: Lecture Notes in Electrical Engineering).
Abstract | Links | BibTeX | Tags: Dialogue, Natural Language, UARC, Virtual Humans
@incollection{dharo_towards_2021,
title = {Towards Personalization of Spoken Dialogue System Communication Strategies},
author = {Carla Gordon and Kallirroi Georgila and Volodymyr Yanov and David Traum},
editor = {Luis Fernando D'Haro and Zoraida Callejas and Satoshi Nakamura},
url = {http://link.springer.com/10.1007/978-981-15-8395-7_11},
doi = {10.1007/978-981-15-8395-7_11},
isbn = {9789811583940 9789811583957},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
booktitle = {Conversational Dialogue Systems for the Next Decade},
volume = {704},
pages = {145--160},
publisher = {Springer Singapore},
address = {Singapore},
abstract = {This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.},
note = {Series Title: Lecture Notes in Electrical Engineering},
keywords = {Dialogue, Natural Language, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.
Gervits, Felix; Leuski, Anton; Bonial, Claire; Gordon, Carla; Traum, David
A Classification-Based Approach to Automating Human-Robot Dialogue Book Section
In: Marchi, Erik; Siniscalchi, Sabato Marco; Cumani, Sandro; Salerno, Valerio Mario; Li, Haizhou (Ed.): Increasing Naturalness and Flexibility in Spoken Dialogue Interaction: 10th International Workshop on Spoken Dialogue Systems, pp. 115–127, Springer, Singapore, 2021, ISBN: 9789811593239.
Abstract | Links | BibTeX | Tags: Dialogue, DTIC
@incollection{gervits_classification-based_2021,
title = {A Classification-Based Approach to Automating Human-Robot Dialogue},
author = {Felix Gervits and Anton Leuski and Claire Bonial and Carla Gordon and David Traum},
editor = {Erik Marchi and Sabato Marco Siniscalchi and Sandro Cumani and Valerio Mario Salerno and Haizhou Li},
url = {https://doi.org/10.1007/978-981-15-9323-9_10},
doi = {10.1007/978-981-15-9323-9_10},
isbn = {9789811593239},
year = {2021},
date = {2021-01-01},
urldate = {2022-09-23},
booktitle = {Increasing Naturalness and Flexibility in Spoken Dialogue Interaction: 10th International Workshop on Spoken Dialogue Systems},
pages = {115–127},
publisher = {Springer},
address = {Singapore},
series = {Lecture Notes in Electrical Engineering},
abstract = {We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multi-floor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.},
keywords = {Dialogue, DTIC},
pubstate = {published},
tppubtype = {incollection}
}
We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multi-floor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.
He, Zihao; Tavabi, Leili; Lerman, Kristina; Soleymani, Mohammad
Speaker Turn Modeling for Dialogue Act Classification Proceedings Article
In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 2150–2157, Association for Computational Linguistics, Punta Cana, Dominican Republic, 2021.
Links | BibTeX | Tags: Dialogue, DTIC, UARC
@inproceedings{he_speaker_2021,
title = {Speaker Turn Modeling for Dialogue Act Classification},
author = {Zihao He and Leili Tavabi and Kristina Lerman and Mohammad Soleymani},
url = {https://aclanthology.org/2021.findings-emnlp.185},
doi = {10.18653/v1/2021.findings-emnlp.185},
year = {2021},
date = {2021-01-01},
urldate = {2022-09-23},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2021},
pages = {2150–2157},
publisher = {Association for Computational Linguistics},
address = {Punta Cana, Dominican Republic},
keywords = {Dialogue, DTIC, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Kawano, Seiya; Yoshino, Koichiro; Traum, David; Nakamura, Satoshi
Dialogue Structure Parsing on Multi-Floor Dialogue Based on Multi-Task Learning Proceedings Article
In: 1st RobotDial Workshop on Dialogue Models for Human-Robot Interaction, pp. 21–29, ISCA, 2021.
Abstract | Links | BibTeX | Tags: ARL, Dialogue, DTIC, Natural Language, Virtual Humans
@inproceedings{kawano_dialogue_2021,
title = {Dialogue Structure Parsing on Multi-Floor Dialogue Based on Multi-Task Learning},
author = {Seiya Kawano and Koichiro Yoshino and David Traum and Satoshi Nakamura},
url = {http://www.isca-speech.org/archive/RobotDial_2021/abstracts/4.html},
doi = {10.21437/RobotDial.2021-4},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
booktitle = {1st RobotDial Workshop on Dialogue Models for Human-Robot Interaction},
pages = {21–29},
publisher = {ISCA},
abstract = {A multi-floor dialogue consists of multiple sets of dialogue participants, each conversing within their own floor, but also at least one multicommunicating member who is a participant of multiple floors and coordinating each to achieve a shared dialogue goal. The structure of such dialogues can be complex, involving intentional structure and relations that are within or across floors. In this study, we propose a neural dialogue structure parser based on multi-task learning and an attention mechanism on multi-floor dialogues in a collaborative robot navigation domain. Our experimental results show that our proposed model improved the dialogue structure parsing performance more than those of single models, which are trained on each dialogue structure parsing task in multi-floor dialogues.},
keywords = {ARL, Dialogue, DTIC, Natural Language, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
A multi-floor dialogue consists of multiple sets of dialogue participants, each conversing within their own floor, but also at least one multicommunicating member who is a participant of multiple floors and coordinating each to achieve a shared dialogue goal. The structure of such dialogues can be complex, involving intentional structure and relations that are within or across floors. In this study, we propose a neural dialogue structure parser based on multi-task learning and an attention mechanism on multi-floor dialogues in a collaborative robot navigation domain. Our experimental results show that our proposed model improved the dialogue structure parsing performance more than those of single models, which are trained on each dialogue structure parsing task in multi-floor dialogues.
2020
Gordon, Carla; Georgila, Kallirroi; Yanov, Volodymyr; Traum, David
Towards Personalization of Spoken Dialogue System Communication Strategies Book Section
In: Conversational Dialogue Systems for the Next Decade, vol. 704, pp. 145–160, Springer Singapore, Singapore, 2020, ISBN: 9789811583940 9789811583957.
Abstract | Links | BibTeX | Tags: ARO-Coop, Dialogue, Natural Language, UARC, Virtual Humans
@incollection{gordon_towards_2020,
title = {Towards Personalization of Spoken Dialogue System Communication Strategies},
author = {Carla Gordon and Kallirroi Georgila and Volodymyr Yanov and David Traum},
url = {http://link.springer.com/10.1007/978-981-15-8395-7_11},
isbn = {9789811583940 9789811583957},
year = {2020},
date = {2020-09-01},
booktitle = {Conversational Dialogue Systems for the Next Decade},
volume = {704},
pages = {145–160},
publisher = {Springer Singapore},
address = {Singapore},
abstract = {This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.},
keywords = {ARO-Coop, Dialogue, Natural Language, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.
0000
Gervits, Felix; Leuski, Anton; Bonial, Claire; Gordon, Carla; Traum, David
A Classification-Based Approach to Automating Human-Robot Dialogue Journal Article
In: pp. 13, 0000.
Abstract | Links | BibTeX | Tags: ARL, Dialogue, UARC, Virtual Humans
@article{gervits_classication-based_nodate,
title = {A Classification-Based Approach to Automating Human-Robot Dialogue},
author = {Felix Gervits and Anton Leuski and Claire Bonial and Carla Gordon and David Traum},
url = {https://link.springer.com/chapter/10.1007/978-981-15-9323-9_10},
doi = {https://doi.org/10.1007/978-981-15-9323-9_10},
pages = {13},
abstract = {We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multifloor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.},
keywords = {ARL, Dialogue, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multifloor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.