Guess What: A Question Answering Game via On-demand Knowledge Validation

Yu-Sheng Li, Chien-Hui Tseng, Chian-Yun Huang, Wei-Yun Ma


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.
Anthology ID:
I17-3015
Volume:
Proceedings of the IJCNLP 2017, System Demonstrations
Month:
November
Year:
2017
Address:
Tapei, Taiwan
Editors:
Seong-Bae Park, Thepchai Supnithi
Venue:
IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
57–60
Language:
URL:
https://aclanthology.org/I17-3015
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Guess What: A Question Answering Game via On-demand Knowledge Validation (Li et al., IJCNLP 2017)
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PDF:
https://aclanthology.org/I17-3015.pdf