Question-Answering with Logic Specific to Video Games

Corentin Dumont, Ran Tian, Kentaro Inui


Abstract
We present a corpus and a knowledge database aiming at developing Question-Answering in a new context, the open world of a video game. We chose a popular game called ‘Minecraft’, and created a QA corpus with a knowledge database related to this game and the ontology of a meaning representation that will be used to structure this database. We are interested in the logic rules specific to the game, which may not exist in the real world. The ultimate goal of this research is to build a QA system that can answer natural language questions from players by using inference on these game-specific logic rules. The QA corpus is partially composed of online quiz questions and partially composed of manually written variations of the most relevant ones. The knowledge database is extracted from several wiki-like websites about Minecraft. It is composed of unstructured data, such as text, that will be structured using the meaning representation we defined, and already structured data such as infoboxes. A preliminary examination of the data shows that players are asking creative questions about the game, and that the QA corpus can be used for clustering verbs and linking them to predefined actions in the game.
Anthology ID:
L16-1734
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
4637–4643
Language:
URL:
https://aclanthology.org/L16-1734
DOI:
Bibkey:
Cite (ACL):
Corentin Dumont, Ran Tian, and Kentaro Inui. 2016. Question-Answering with Logic Specific to Video Games. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 4637–4643, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Question-Answering with Logic Specific to Video Games (Dumont et al., LREC 2016)
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PDF:
https://aclanthology.org/L16-1734.pdf