@inproceedings{qiu-etal-2022-towards,
title = "Towards Socially Intelligent Agents with Mental State Transition and Human Value",
author = "Qiu, Liang and
Zhao, Yizhou and
Liang, Yuan and
Lu, Pan and
Shi, Weiyan and
Yu, Zhou and
Zhu, Song-Chun",
editor = "Lemon, Oliver and
Hakkani-Tur, Dilek and
Li, Junyi Jessy and
Ashrafzadeh, Arash and
Garcia, Daniel Hern{\'a}ndez and
Alikhani, Malihe and
Vandyke, David and
Du{\v{s}}ek, Ond{\v{r}}ej",
booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2022",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigdial-1.16",
doi = "10.18653/v1/2022.sigdial-1.16",
pages = "146--158",
abstract = "Building a socially intelligent agent involves many challenges. One of which is to track the agent{'}s mental state transition and teach the agent to make decisions guided by its value like a human. Towards this end, we propose to incorporate mental state simulation and value modeling into dialogue agents. First, we build a hybrid mental state parser that extracts information from both the dialogue and event observations and maintains a graphical representation of the agent{'}s mind; Meanwhile, the transformer-based value model learns human preferences from the human value dataset, ValueNet. Empirical results show that the proposed model attains state-of-the-art performance on the dialogue/action/emotion prediction task in the fantasy text-adventure game dataset, LIGHT. We also show example cases to demonstrate: (i) how the proposed mental state parser can assist the agent{'}s decision by grounding on the context like locations and objects, and (ii) how the value model can help the agent make decisions based on its personal priorities.",
}
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<abstract>Building a socially intelligent agent involves many challenges. One of which is to track the agent’s mental state transition and teach the agent to make decisions guided by its value like a human. Towards this end, we propose to incorporate mental state simulation and value modeling into dialogue agents. First, we build a hybrid mental state parser that extracts information from both the dialogue and event observations and maintains a graphical representation of the agent’s mind; Meanwhile, the transformer-based value model learns human preferences from the human value dataset, ValueNet. Empirical results show that the proposed model attains state-of-the-art performance on the dialogue/action/emotion prediction task in the fantasy text-adventure game dataset, LIGHT. We also show example cases to demonstrate: (i) how the proposed mental state parser can assist the agent’s decision by grounding on the context like locations and objects, and (ii) how the value model can help the agent make decisions based on its personal priorities.</abstract>
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%0 Conference Proceedings
%T Towards Socially Intelligent Agents with Mental State Transition and Human Value
%A Qiu, Liang
%A Zhao, Yizhou
%A Liang, Yuan
%A Lu, Pan
%A Shi, Weiyan
%A Yu, Zhou
%A Zhu, Song-Chun
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F qiu-etal-2022-towards
%X Building a socially intelligent agent involves many challenges. One of which is to track the agent’s mental state transition and teach the agent to make decisions guided by its value like a human. Towards this end, we propose to incorporate mental state simulation and value modeling into dialogue agents. First, we build a hybrid mental state parser that extracts information from both the dialogue and event observations and maintains a graphical representation of the agent’s mind; Meanwhile, the transformer-based value model learns human preferences from the human value dataset, ValueNet. Empirical results show that the proposed model attains state-of-the-art performance on the dialogue/action/emotion prediction task in the fantasy text-adventure game dataset, LIGHT. We also show example cases to demonstrate: (i) how the proposed mental state parser can assist the agent’s decision by grounding on the context like locations and objects, and (ii) how the value model can help the agent make decisions based on its personal priorities.
%R 10.18653/v1/2022.sigdial-1.16
%U https://aclanthology.org/2022.sigdial-1.16
%U https://doi.org/10.18653/v1/2022.sigdial-1.16
%P 146-158
Markdown (Informal)
[Towards Socially Intelligent Agents with Mental State Transition and Human Value](https://aclanthology.org/2022.sigdial-1.16) (Qiu et al., SIGDIAL 2022)
ACL