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21.
Georgila, Kallirroi; Gordon, Carla; Yanov, Volodymyr; Traum, David
Predicting Ratings of Real Dialogue Participants from Artificial Data and Ratings of Human Dialogue Observers Proceedings Article
In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 726–734, European Language Resources Association, Marseille, France, 2020, ISBN: 979-10-95546-34-4.
@inproceedings{georgila_predicting_2020,
title = {Predicting Ratings of Real Dialogue Participants from Artificial Data and Ratings of Human Dialogue Observers},
author = {Kallirroi Georgila and Carla Gordon and Volodymyr Yanov and David Traum},
url = {https://aclanthology.org/2020.lrec-1.91},
isbn = {979-10-95546-34-4},
year = {2020},
date = {2020-05-01},
urldate = {2023-03-31},
booktitle = {Proceedings of the Twelfth Language Resources and Evaluation Conference},
pages = {726–734},
publisher = {European Language Resources Association},
address = {Marseille, France},
abstract = {We collected a corpus of dialogues in a Wizard of Oz (WOz) setting in the Internet of Things (IoT) domain. We asked users participating in these dialogues to rate the system on a number of aspects, namely, intelligence, naturalness, personality, friendliness, their enjoyment, overall quality, and whether they would recommend the system to others. Then we asked dialogue observers, i.e., Amazon Mechanical Turkers (MTurkers), to rate these dialogues on the same aspects. We also generated simulated dialogues between dialogue policies and simulated users and asked MTurkers to rate them again on the same aspects. Using linear regression, we developed dialogue evaluation functions based on features from the simulated dialogues and the MTurkers' ratings, the WOz dialogues and the MTurkers' ratings, and the WOz dialogues and the WOz participants' ratings. We applied all these dialogue evaluation functions to a held-out portion of our WOz dialogues, and we report results on the predictive power of these different types of dialogue evaluation functions. Our results suggest that for three conversational aspects (intelligence, naturalness, overall quality) just training evaluation functions on simulated data could be sufficient.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We collected a corpus of dialogues in a Wizard of Oz (WOz) setting in the Internet of Things (IoT) domain. We asked users participating in these dialogues to rate the system on a number of aspects, namely, intelligence, naturalness, personality, friendliness, their enjoyment, overall quality, and whether they would recommend the system to others. Then we asked dialogue observers, i.e., Amazon Mechanical Turkers (MTurkers), to rate these dialogues on the same aspects. We also generated simulated dialogues between dialogue policies and simulated users and asked MTurkers to rate them again on the same aspects. Using linear regression, we developed dialogue evaluation functions based on features from the simulated dialogues and the MTurkers' ratings, the WOz dialogues and the MTurkers' ratings, and the WOz dialogues and the WOz participants' ratings. We applied all these dialogue evaluation functions to a held-out portion of our WOz dialogues, and we report results on the predictive power of these different types of dialogue evaluation functions. Our results suggest that for three conversational aspects (intelligence, naturalness, overall quality) just training evaluation functions on simulated data could be sufficient.
22.
Shmueli-Scheuer, Michal; Artstein, Ron; Khazaeni, Yasaman; Fang, Hao; Liao, Q. Vera
user2agent: 2nd Workshop on User-Aware Conversational Agents Proceedings Article
In: Proceedings of the 25th International Conference on Intelligent User Interfaces Companion, pp. 9–10, Association for Computing Machinery, New York, NY, USA, 2020, ISBN: 978-1-4503-7513-9.
@inproceedings{shmueli-scheuer_user2agent_2020,
title = {user2agent: 2nd Workshop on User-Aware Conversational Agents},
author = {Michal Shmueli-Scheuer and Ron Artstein and Yasaman Khazaeni and Hao Fang and Q. Vera Liao},
url = {https://doi.org/10.1145/3379336.3379356},
doi = {10.1145/3379336.3379356},
isbn = {978-1-4503-7513-9},
year = {2020},
date = {2020-03-01},
urldate = {2023-03-31},
booktitle = {Proceedings of the 25th International Conference on Intelligent User Interfaces Companion},
pages = {9–10},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {IUI '20},
abstract = {Conversational agents are becoming increasingly popular. These systems present an extremely rich and challenging research space for addressing many aspects of user awareness and adaptation, such as user profiles, contexts, personalities, emotions, social dynamics, conversational styles, etc. Adaptive interfaces are of long-standing interest for the HCI community. Meanwhile, new machine learning approaches are introduced in the current generation of conversational agents, such as deep learning, reinforcement learning, and active learning. It is imperative to consider how various aspects of user-awareness should be handled by these new techniques. The goal of this workshop is to bring together researchers in HCI, user modeling, and the AI and NLP communities from both industry and academia, who are interested in advancing the state-of-the-art on the topic of user-aware conversational agents. Through a focused and open exchange of ideas and discussions, we will work to identify central research topics in user-aware conversational agents and develop a strong interdisciplinary foundation to address them.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Conversational agents are becoming increasingly popular. These systems present an extremely rich and challenging research space for addressing many aspects of user awareness and adaptation, such as user profiles, contexts, personalities, emotions, social dynamics, conversational styles, etc. Adaptive interfaces are of long-standing interest for the HCI community. Meanwhile, new machine learning approaches are introduced in the current generation of conversational agents, such as deep learning, reinforcement learning, and active learning. It is imperative to consider how various aspects of user-awareness should be handled by these new techniques. The goal of this workshop is to bring together researchers in HCI, user modeling, and the AI and NLP communities from both industry and academia, who are interested in advancing the state-of-the-art on the topic of user-aware conversational agents. Through a focused and open exchange of ideas and discussions, we will work to identify central research topics in user-aware conversational agents and develop a strong interdisciplinary foundation to address them.
23.
Shinagawa, Seitaro; Yoshino, Koichiro; Alavi, Seyed Hossein; Georgila, Kallirroi; Traum, David; Sakti, Sakriani; Nakamura, Satoshi
An Interactive Image Editing System Using an Uncertainty-Based Confirmation Strategy Journal Article
In: IEEE Access, vol. 8, pp. 98471–98480, 2020, ISSN: 2169-3536, (Conference Name: IEEE Access).
@article{shinagawa_interactive_2020,
title = {An Interactive Image Editing System Using an Uncertainty-Based Confirmation Strategy},
author = {Seitaro Shinagawa and Koichiro Yoshino and Seyed Hossein Alavi and Kallirroi Georgila and David Traum and Sakriani Sakti and Satoshi Nakamura},
url = {https://ieeexplore.ieee.org/abstract/document/9099288},
doi = {10.1109/ACCESS.2020.2997012},
issn = {2169-3536},
year = {2020},
date = {2020-01-01},
journal = {IEEE Access},
volume = {8},
pages = {98471–98480},
abstract = {We propose an interactive image editing system that has a confirmation dialogue strategy using an entropy-based uncertainty calculation on its generated images with Deep Convolutional Generative Adversarial Networks (DCGAN). DCGAN is an image generative model that learns an image manifold of a given dataset and enables continuous change of an image. Our proposed image editing system combines DCGAN with a natural language interface that accepts image editing requests in natural language. Although such a system is helpful for human users, it often faces uncertain requests to generate acceptable images. A promising approach to solve this problem is introducing a dialogue process that shows multiple candidates and confirms the user's intention. However, confirming every editing request creates redundant dialogues. To achieve more efficient dialogues, we propose an entropy-based dialogue strategy that decides when the system should confirm, and enables effective image editing through a dialogue that reduces redundant confirmations. We conducted image editing dialogue experiments using an avatar face illustration dataset for editing by natural language requests. Through quantitative and qualitative analysis, our results show that our entropy-based confirmation strategy achieved an effective dialogue by generating images desired by users.},
note = {Conference Name: IEEE Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We propose an interactive image editing system that has a confirmation dialogue strategy using an entropy-based uncertainty calculation on its generated images with Deep Convolutional Generative Adversarial Networks (DCGAN). DCGAN is an image generative model that learns an image manifold of a given dataset and enables continuous change of an image. Our proposed image editing system combines DCGAN with a natural language interface that accepts image editing requests in natural language. Although such a system is helpful for human users, it often faces uncertain requests to generate acceptable images. A promising approach to solve this problem is introducing a dialogue process that shows multiple candidates and confirms the user's intention. However, confirming every editing request creates redundant dialogues. To achieve more efficient dialogues, we propose an entropy-based dialogue strategy that decides when the system should confirm, and enables effective image editing through a dialogue that reduces redundant confirmations. We conducted image editing dialogue experiments using an avatar face illustration dataset for editing by natural language requests. Through quantitative and qualitative analysis, our results show that our entropy-based confirmation strategy achieved an effective dialogue by generating images desired by users.
24.
Uryupina, Olga; Artstein, Ron; Bristot, Antonella; Cavicchio, Federica; Delogu, Francesca; Rodriguez, Kepa J.; Poesio, Massimo
Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU Corpus Journal Article
In: Natural Language Engineering, vol. 26, no. 1, pp. 95–128, 2020, ISSN: 1351-3249, 1469-8110, (Publisher: Cambridge University Press).
@article{uryupina_annotating_2020,
title = {Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU Corpus},
author = {Olga Uryupina and Ron Artstein and Antonella Bristot and Federica Cavicchio and Francesca Delogu and Kepa J. Rodriguez and Massimo Poesio},
url = {https://www.cambridge.org/core/journals/natural-language-engineering/article/abs/annotating-a-broad-range-of-anaphoric-phenomena-in-a-variety-of-genres-the-arrau-corpus/17E7FA2CB2E36C213E2649479593B6B0},
doi = {10.1017/S1351324919000056},
issn = {1351-3249, 1469-8110},
year = {2020},
date = {2020-01-01},
urldate = {2023-03-31},
journal = {Natural Language Engineering},
volume = {26},
number = {1},
pages = {95–128},
abstract = {This paper presents the second release of arrau, a multigenre corpus of anaphoric information created over 10 years to provide data for the next generation of coreference/anaphora resolution systems combining different types of linguistic and world knowledge with advanced discourse modeling supporting rich linguistic annotations. The distinguishing features of arrau include the following: treating all NPs as markables, including non-referring NPs, and annotating their (non-) referentiality status; distinguishing between several categories of non-referentiality and annotating non-anaphoric mentions; thorough annotation of markable boundaries (minimal/maximal spans, discontinuous markables); annotating a variety of mention attributes, ranging from morphosyntactic parameters to semantic category; annotating the genericity status of mentions; annotating a wide range of anaphoric relations, including bridging relations and discourse deixis; and, finally, annotating anaphoric ambiguity. The current version of the dataset contains 350K tokens and is publicly available from LDC. In this paper, we discuss in detail all the distinguishing features of the corpus, so far only partially presented in a number of conference and workshop papers, and we also discuss the development between the first release of arrau in 2008 and this second one.},
note = {Publisher: Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper presents the second release of arrau, a multigenre corpus of anaphoric information created over 10 years to provide data for the next generation of coreference/anaphora resolution systems combining different types of linguistic and world knowledge with advanced discourse modeling supporting rich linguistic annotations. The distinguishing features of arrau include the following: treating all NPs as markables, including non-referring NPs, and annotating their (non-) referentiality status; distinguishing between several categories of non-referentiality and annotating non-anaphoric mentions; thorough annotation of markable boundaries (minimal/maximal spans, discontinuous markables); annotating a variety of mention attributes, ranging from morphosyntactic parameters to semantic category; annotating the genericity status of mentions; annotating a wide range of anaphoric relations, including bridging relations and discourse deixis; and, finally, annotating anaphoric ambiguity. The current version of the dataset contains 350K tokens and is publicly available from LDC. In this paper, we discuss in detail all the distinguishing features of the corpus, so far only partially presented in a number of conference and workshop papers, and we also discuss the development between the first release of arrau in 2008 and this second one.
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