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        <title>Guess What: A Question Answering Game via On-demand Knowledge Validation</title>
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        <namePart type="given">Yu-Sheng</namePart>
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    <name type="personal">
        <namePart type="given">Chien-Hui</namePart>
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    <name type="personal">
        <namePart type="given">Chian-Yun</namePart>
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    <name type="personal">
        <namePart type="given">Wei-Yun</namePart>
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    <abstract>In this paper, we propose an idea of ondemand knowledge validation and fulfill the idea through an interactive Question-Answering (QA) game system, which is named Guess What. An object (e.g. dog) is first randomly chosen by the system, and then a user can repeatedly ask the system questions in natural language to guess what the object is. The system would respond with yes/no along with a confidence score. Some useful hints can also be given if needed. The proposed framework provides a pioneering example of on-demand knowledge validation in dialog environment to address such needs in AI agents/chatbots. Moreover, the released log data that the system gathered can be used to identify the most critical concepts/attributes of an existing knowledge base, which reflects human’s cognition about the world.</abstract>
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        <date>2017-11</date>
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%0 Conference Proceedings
%T Guess What: A Question Answering Game via On-demand Knowledge Validation
%A Li, Yu-Sheng
%A Tseng, Chien-Hui
%A Huang, Chian-Yun
%A Ma, Wei-Yun
%Y Park, Seong-Bae
%Y Supnithi, Thepchai
%S Proceedings of the IJCNLP 2017, System Demonstrations
%D 2017
%8 November
%I Association for Computational Linguistics
%C Tapei, Taiwan
%F li-etal-2017-guess
%X In this paper, we propose an idea of ondemand knowledge validation and fulfill the idea through an interactive Question-Answering (QA) game system, which is named Guess What. An object (e.g. dog) is first randomly chosen by the system, and then a user can repeatedly ask the system questions in natural language to guess what the object is. The system would respond with yes/no along with a confidence score. Some useful hints can also be given if needed. The proposed framework provides a pioneering example of on-demand knowledge validation in dialog environment to address such needs in AI agents/chatbots. Moreover, the released log data that the system gathered can be used to identify the most critical concepts/attributes of an existing knowledge base, which reflects human’s cognition about the world.
%U https://aclanthology.org/I17-3015
%P 57-60

Markdown (Informal)

[Guess What: A Question Answering Game via On-demand Knowledge Validation](https://aclanthology.org/I17-3015) (Li et al., IJCNLP 2017)

• Guess What: A Question Answering Game via On-demand Knowledge Validation (Li et al., IJCNLP 2017)

ACL

• Yu-Sheng Li, Chien-Hui Tseng, Chian-Yun Huang, and Wei-Yun Ma. 2017. Guess What: A Question Answering Game via On-demand Knowledge Validation. In Proceedings of the IJCNLP 2017, System Demonstrations, pages 57–60, Tapei, Taiwan. Association for Computational Linguistics.