Madly Ambiguous: A Game for Learning about Structural Ambiguity and Why It’s Hard for Computers

Ajda Gokcen, Ethan Hill, Michael White


Abstract
Madly Ambiguous is an open source, online game aimed at teaching audiences of all ages about structural ambiguity and why it’s hard for computers. After a brief introduction to structural ambiguity, users are challenged to complete a sentence in a way that tricks the computer into guessing an incorrect interpretation. Behind the scenes are two different NLP-based methods for classifying the user’s input, one representative of classic rule-based approaches to disambiguation and the other representative of recent neural network approaches. Qualitative feedback from the system’s use in online, classroom, and science museum settings indicates that it is engaging and successful in conveying the intended take home messages. A demo of Madly Ambiguous can be played at http://madlyambiguous.osu.edu.
Anthology ID:
N18-5011
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Yang Liu, Tim Paek, Manasi Patwardhan
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
51–55
Language:
URL:
https://aclanthology.org/N18-5011
DOI:
10.18653/v1/N18-5011
Bibkey:
Cite (ACL):
Ajda Gokcen, Ethan Hill, and Michael White. 2018. Madly Ambiguous: A Game for Learning about Structural Ambiguity and Why It’s Hard for Computers. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 51–55, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Madly Ambiguous: A Game for Learning about Structural Ambiguity and Why It’s Hard for Computers (Gokcen et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-5011.pdf