Publications
Search
1.
Brixey, Jacqueline; Traum, David
Masheli: A Choctaw-English bilingual chatbot Book Section
In: Conversational Dialogue Systems for the Next Decade, pp. 41–50, Springer, Switzerland, 2020.
@incollection{brixey_masheli_2020,
title = {Masheli: A Choctaw-English bilingual chatbot},
author = {Jacqueline Brixey and David Traum},
url = {https://link.springer.com/chapter/10.1007/978-981-15-8395-7_4},
year = {2020},
date = {2020-10-01},
booktitle = {Conversational Dialogue Systems for the Next Decade},
pages = {41–50},
publisher = {Springer},
address = {Switzerland},
abstract = {We present the implementation of an autonomous Choctaw-English bilingual chatbot. Choctaw is an American indigenous language. The intended use of the chatbot is for Choctaw language learners to pratice conversational skills. The system’s backend is NPCEditor, a response selection program that is trained on linked questions and answers. The chatbot’s answers are stories and conversational utterances in both languages. We experiment with the ability of NPCEditor to appropriately respond to language mixed utterances, and describe a pilot study with Choctaw-English speakers.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
We present the implementation of an autonomous Choctaw-English bilingual chatbot. Choctaw is an American indigenous language. The intended use of the chatbot is for Choctaw language learners to pratice conversational skills. The system’s backend is NPCEditor, a response selection program that is trained on linked questions and answers. The chatbot’s answers are stories and conversational utterances in both languages. We experiment with the ability of NPCEditor to appropriately respond to language mixed utterances, and describe a pilot study with Choctaw-English speakers.
2.
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: 978-981-15-8394-0 978-981-15-8395-7.
@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 = {978-981-15-8394-0 978-981-15-8395-7},
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.
3.
Brixey, Jacqueline; Artstein, Ron
ChoCo: a multimodal corpus of the Choctaw language Journal Article
In: Language Resources and Evaluation, 2020, ISSN: 1574-020X, 1574-0218.
@article{brixey_choco_2020,
title = {ChoCo: a multimodal corpus of the Choctaw language},
author = {Jacqueline Brixey and Ron Artstein},
url = {http://link.springer.com/10.1007/s10579-020-09494-5},
doi = {10.1007/s10579-020-09494-5},
issn = {1574-020X, 1574-0218},
year = {2020},
date = {2020-07-01},
journal = {Language Resources and Evaluation},
abstract = {This article presents a general use corpus for Choctaw, an American indigenous language (ISO 639-2: cho, endonym: Chahta). The corpus contains audio, video, and text resources, with many texts also translated in English. The Oklahoma Choctaw and the Mississippi Choctaw variants of the language are represented in the corpus. The data set provides documentation support for this threatened language, and allows researchers and language teachers access to a diverse collection of resources.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article presents a general use corpus for Choctaw, an American indigenous language (ISO 639-2: cho, endonym: Chahta). The corpus contains audio, video, and text resources, with many texts also translated in English. The Oklahoma Choctaw and the Mississippi Choctaw variants of the language are represented in the corpus. The data set provides documentation support for this threatened language, and allows researchers and language teachers access to a diverse collection of resources.
4.
Czyzewski, Adam; Dalton, Jeffrey; Leuski, Anton
Agent Dialogue: A Platform for Conversational Information Seeking Experimentation Proceedings Article
In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2121–2124, ACM, Virtual Event China, 2020, ISBN: 978-1-4503-8016-4.
@inproceedings{czyzewski_agent_2020,
title = {Agent Dialogue: A Platform for Conversational Information Seeking Experimentation},
author = {Adam Czyzewski and Jeffrey Dalton and Anton Leuski},
url = {https://dl.acm.org/doi/10.1145/3397271.3401397},
doi = {10.1145/3397271.3401397},
isbn = {978-1-4503-8016-4},
year = {2020},
date = {2020-07-01},
booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2121–2124},
publisher = {ACM},
address = {Virtual Event China},
abstract = {Conversational Information Seeking (CIS) is an emerging area of Information Retrieval focused on interactive search systems. As a result there is a need for new benchmark datasets and tools to enable their creation. In this demo we present the Agent Dialogue (AD) platform, an open-source system developed for researchers to perform Wizard-of-Oz CIS experiments. AD is a scalable cloud-native platform developed with Docker and Kubernetes with a flexible and modular micro-service architecture built on production-grade stateof-the-art open-source tools (Kubernetes, gRPC streaming, React, and Firebase). It supports varied front-ends and has the ability to interface with multiple existing agent systems, including Google Assistant and open-source search libraries. It includes support for centralized structure logging as well as offline relevance annotation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Conversational Information Seeking (CIS) is an emerging area of Information Retrieval focused on interactive search systems. As a result there is a need for new benchmark datasets and tools to enable their creation. In this demo we present the Agent Dialogue (AD) platform, an open-source system developed for researchers to perform Wizard-of-Oz CIS experiments. AD is a scalable cloud-native platform developed with Docker and Kubernetes with a flexible and modular micro-service architecture built on production-grade stateof-the-art open-source tools (Kubernetes, gRPC streaming, React, and Firebase). It supports varied front-ends and has the ability to interface with multiple existing agent systems, including Google Assistant and open-source search libraries. It includes support for centralized structure logging as well as offline relevance annotation.
5.
Alavi, Seyed Hossein; Leuski, Anton; Traum, David
Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net? Proceedings Article
In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 735–742, European Language Resources Association, Marseille, France, 2020.
@inproceedings{alavi_which_2020,
title = {Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net?},
author = {Seyed Hossein Alavi and Anton Leuski and David Traum},
url = {https://www.aclweb.org/anthology/2020.lrec-1.92/},
year = {2020},
date = {2020-05-01},
booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},
pages = {735–742},
publisher = {European Language Resources Association},
address = {Marseille, France},
abstract = {We compare two models for corpus-based selection of dialogue responses: one based on cross-language relevance with a cross-language LSTM model. Each model is tested on multiple corpora, collected from two different types of dialogue source material. Results show that while the LSTM model performs adequately on a very large corpus (millions of utterances), its performance is dominated by the cross-language relevance model for a more moderate-sized corpus (ten thousands of utterances).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We compare two models for corpus-based selection of dialogue responses: one based on cross-language relevance with a cross-language LSTM model. Each model is tested on multiple corpora, collected from two different types of dialogue source material. Results show that while the LSTM model performs adequately on a very large corpus (millions of utterances), its performance is dominated by the cross-language relevance model for a more moderate-sized corpus (ten thousands of utterances).
6.
Bonial, Claire; Donatelli, Lucia; Abrams, Mitchell; Lukin, Stephanie M; Tratz, Stephen; Marge, Matthew; Artstein, Ron; Traum, David; Voss, Clare R
Dialogue-AMR: Abstract Meaning Representation for Dialogue Proceedings Article
In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 12, European Language Resources Association, Marseille, France, 2020.
@inproceedings{bonial_dialogue-amr_2020,
title = {Dialogue-AMR: Abstract Meaning Representation for Dialogue},
author = {Claire Bonial and Lucia Donatelli and Mitchell Abrams and Stephanie M Lukin and Stephen Tratz and Matthew Marge and Ron Artstein and David Traum and Clare R Voss},
url = {https://www.aclweb.org/anthology/2020.lrec-1.86/},
year = {2020},
date = {2020-05-01},
booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},
pages = {12},
publisher = {European Language Resources Association},
address = {Marseille, France},
abstract = {This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. AMR offers a valuable level of abstraction of the propositional content of an utterance; however, it does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context (e.g., make a request or ask a question), nor does it capture tense or aspect. We explore dialogue in the domain of human-robot interaction, where a conversational robot is engaged in search and navigation tasks with a human partner. To address the limitations of standard AMR, we develop an inventory of speech acts suitable for our domain, and present “Dialogue-AMR”, an enhanced AMR that represents not only the content of an utterance, but the illocutionary force behind it, as well as tense and aspect. To showcase the coverage of the schema, we use both manual and automatic methods to construct the “DialAMR” corpus—a corpus of human-robot dialogue annotated with standard AMR and our enriched Dialogue-AMR schema. Our automated methods can be used to incorporate AMR into a larger NLU pipeline supporting human-robot dialogue.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. AMR offers a valuable level of abstraction of the propositional content of an utterance; however, it does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context (e.g., make a request or ask a question), nor does it capture tense or aspect. We explore dialogue in the domain of human-robot interaction, where a conversational robot is engaged in search and navigation tasks with a human partner. To address the limitations of standard AMR, we develop an inventory of speech acts suitable for our domain, and present “Dialogue-AMR”, an enhanced AMR that represents not only the content of an utterance, but the illocutionary force behind it, as well as tense and aspect. To showcase the coverage of the schema, we use both manual and automatic methods to construct the “DialAMR” corpus—a corpus of human-robot dialogue annotated with standard AMR and our enriched Dialogue-AMR schema. Our automated methods can be used to incorporate AMR into a larger NLU pipeline supporting human-robot dialogue.
Filter
2020
Brixey, Jacqueline; Traum, David
Masheli: A Choctaw-English bilingual chatbot Book Section
In: Conversational Dialogue Systems for the Next Decade, pp. 41–50, Springer, Switzerland, 2020.
Abstract | Links | BibTeX | Tags: ARO-Coop, Natural Language, UARC, Virtual Humans
@incollection{brixey_masheli_2020,
title = {Masheli: A Choctaw-English bilingual chatbot},
author = {Jacqueline Brixey and David Traum},
url = {https://link.springer.com/chapter/10.1007/978-981-15-8395-7_4},
year = {2020},
date = {2020-10-01},
booktitle = {Conversational Dialogue Systems for the Next Decade},
pages = {41–50},
publisher = {Springer},
address = {Switzerland},
abstract = {We present the implementation of an autonomous Choctaw-English bilingual chatbot. Choctaw is an American indigenous language. The intended use of the chatbot is for Choctaw language learners to pratice conversational skills. The system’s backend is NPCEditor, a response selection program that is trained on linked questions and answers. The chatbot’s answers are stories and conversational utterances in both languages. We experiment with the ability of NPCEditor to appropriately respond to language mixed utterances, and describe a pilot study with Choctaw-English speakers.},
keywords = {ARO-Coop, Natural Language, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
We present the implementation of an autonomous Choctaw-English bilingual chatbot. Choctaw is an American indigenous language. The intended use of the chatbot is for Choctaw language learners to pratice conversational skills. The system’s backend is NPCEditor, a response selection program that is trained on linked questions and answers. The chatbot’s answers are stories and conversational utterances in both languages. We experiment with the ability of NPCEditor to appropriately respond to language mixed utterances, and describe a pilot study with Choctaw-English speakers.
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: 978-981-15-8394-0 978-981-15-8395-7.
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 = {978-981-15-8394-0 978-981-15-8395-7},
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.
Brixey, Jacqueline; Artstein, Ron
ChoCo: a multimodal corpus of the Choctaw language Journal Article
In: Language Resources and Evaluation, 2020, ISSN: 1574-020X, 1574-0218.
Abstract | Links | BibTeX | Tags: ARO-Coop, UARC, Virtual Humans
@article{brixey_choco_2020,
title = {ChoCo: a multimodal corpus of the Choctaw language},
author = {Jacqueline Brixey and Ron Artstein},
url = {http://link.springer.com/10.1007/s10579-020-09494-5},
doi = {10.1007/s10579-020-09494-5},
issn = {1574-020X, 1574-0218},
year = {2020},
date = {2020-07-01},
journal = {Language Resources and Evaluation},
abstract = {This article presents a general use corpus for Choctaw, an American indigenous language (ISO 639-2: cho, endonym: Chahta). The corpus contains audio, video, and text resources, with many texts also translated in English. The Oklahoma Choctaw and the Mississippi Choctaw variants of the language are represented in the corpus. The data set provides documentation support for this threatened language, and allows researchers and language teachers access to a diverse collection of resources.},
keywords = {ARO-Coop, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
This article presents a general use corpus for Choctaw, an American indigenous language (ISO 639-2: cho, endonym: Chahta). The corpus contains audio, video, and text resources, with many texts also translated in English. The Oklahoma Choctaw and the Mississippi Choctaw variants of the language are represented in the corpus. The data set provides documentation support for this threatened language, and allows researchers and language teachers access to a diverse collection of resources.
Czyzewski, Adam; Dalton, Jeffrey; Leuski, Anton
Agent Dialogue: A Platform for Conversational Information Seeking Experimentation Proceedings Article
In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2121–2124, ACM, Virtual Event China, 2020, ISBN: 978-1-4503-8016-4.
Abstract | Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@inproceedings{czyzewski_agent_2020,
title = {Agent Dialogue: A Platform for Conversational Information Seeking Experimentation},
author = {Adam Czyzewski and Jeffrey Dalton and Anton Leuski},
url = {https://dl.acm.org/doi/10.1145/3397271.3401397},
doi = {10.1145/3397271.3401397},
isbn = {978-1-4503-8016-4},
year = {2020},
date = {2020-07-01},
booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2121–2124},
publisher = {ACM},
address = {Virtual Event China},
abstract = {Conversational Information Seeking (CIS) is an emerging area of Information Retrieval focused on interactive search systems. As a result there is a need for new benchmark datasets and tools to enable their creation. In this demo we present the Agent Dialogue (AD) platform, an open-source system developed for researchers to perform Wizard-of-Oz CIS experiments. AD is a scalable cloud-native platform developed with Docker and Kubernetes with a flexible and modular micro-service architecture built on production-grade stateof-the-art open-source tools (Kubernetes, gRPC streaming, React, and Firebase). It supports varied front-ends and has the ability to interface with multiple existing agent systems, including Google Assistant and open-source search libraries. It includes support for centralized structure logging as well as offline relevance annotation.},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Conversational Information Seeking (CIS) is an emerging area of Information Retrieval focused on interactive search systems. As a result there is a need for new benchmark datasets and tools to enable their creation. In this demo we present the Agent Dialogue (AD) platform, an open-source system developed for researchers to perform Wizard-of-Oz CIS experiments. AD is a scalable cloud-native platform developed with Docker and Kubernetes with a flexible and modular micro-service architecture built on production-grade stateof-the-art open-source tools (Kubernetes, gRPC streaming, React, and Firebase). It supports varied front-ends and has the ability to interface with multiple existing agent systems, including Google Assistant and open-source search libraries. It includes support for centralized structure logging as well as offline relevance annotation.
Alavi, Seyed Hossein; Leuski, Anton; Traum, David
Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net? Proceedings Article
In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 735–742, European Language Resources Association, Marseille, France, 2020.
Abstract | Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@inproceedings{alavi_which_2020,
title = {Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net?},
author = {Seyed Hossein Alavi and Anton Leuski and David Traum},
url = {https://www.aclweb.org/anthology/2020.lrec-1.92/},
year = {2020},
date = {2020-05-01},
booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},
pages = {735–742},
publisher = {European Language Resources Association},
address = {Marseille, France},
abstract = {We compare two models for corpus-based selection of dialogue responses: one based on cross-language relevance with a cross-language LSTM model. Each model is tested on multiple corpora, collected from two different types of dialogue source material. Results show that while the LSTM model performs adequately on a very large corpus (millions of utterances), its performance is dominated by the cross-language relevance model for a more moderate-sized corpus (ten thousands of utterances).},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
We compare two models for corpus-based selection of dialogue responses: one based on cross-language relevance with a cross-language LSTM model. Each model is tested on multiple corpora, collected from two different types of dialogue source material. Results show that while the LSTM model performs adequately on a very large corpus (millions of utterances), its performance is dominated by the cross-language relevance model for a more moderate-sized corpus (ten thousands of utterances).
Bonial, Claire; Donatelli, Lucia; Abrams, Mitchell; Lukin, Stephanie M; Tratz, Stephen; Marge, Matthew; Artstein, Ron; Traum, David; Voss, Clare R
Dialogue-AMR: Abstract Meaning Representation for Dialogue Proceedings Article
In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 12, European Language Resources Association, Marseille, France, 2020.
Abstract | Links | BibTeX | Tags: ARL, ARO-Coop, DoD, UARC, Virtual Humans
@inproceedings{bonial_dialogue-amr_2020,
title = {Dialogue-AMR: Abstract Meaning Representation for Dialogue},
author = {Claire Bonial and Lucia Donatelli and Mitchell Abrams and Stephanie M Lukin and Stephen Tratz and Matthew Marge and Ron Artstein and David Traum and Clare R Voss},
url = {https://www.aclweb.org/anthology/2020.lrec-1.86/},
year = {2020},
date = {2020-05-01},
booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},
pages = {12},
publisher = {European Language Resources Association},
address = {Marseille, France},
abstract = {This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. AMR offers a valuable level of abstraction of the propositional content of an utterance; however, it does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context (e.g., make a request or ask a question), nor does it capture tense or aspect. We explore dialogue in the domain of human-robot interaction, where a conversational robot is engaged in search and navigation tasks with a human partner. To address the limitations of standard AMR, we develop an inventory of speech acts suitable for our domain, and present “Dialogue-AMR”, an enhanced AMR that represents not only the content of an utterance, but the illocutionary force behind it, as well as tense and aspect. To showcase the coverage of the schema, we use both manual and automatic methods to construct the “DialAMR” corpus—a corpus of human-robot dialogue annotated with standard AMR and our enriched Dialogue-AMR schema. Our automated methods can be used to incorporate AMR into a larger NLU pipeline supporting human-robot dialogue.},
keywords = {ARL, ARO-Coop, DoD, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. AMR offers a valuable level of abstraction of the propositional content of an utterance; however, it does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context (e.g., make a request or ask a question), nor does it capture tense or aspect. We explore dialogue in the domain of human-robot interaction, where a conversational robot is engaged in search and navigation tasks with a human partner. To address the limitations of standard AMR, we develop an inventory of speech acts suitable for our domain, and present “Dialogue-AMR”, an enhanced AMR that represents not only the content of an utterance, but the illocutionary force behind it, as well as tense and aspect. To showcase the coverage of the schema, we use both manual and automatic methods to construct the “DialAMR” corpus—a corpus of human-robot dialogue annotated with standard AMR and our enriched Dialogue-AMR schema. Our automated methods can be used to incorporate AMR into a larger NLU pipeline supporting human-robot dialogue.