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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/I17-3015.pdf