Abstract
Humans as well as animals are good at imitation. Inspired by this, the learning by demonstration view of machine learning learns to perform a task from detailed example demonstrations. In this paper, we introduce the task of question answering using natural language demonstrations where the question answering system is provided with detailed demonstrative solutions to questions in natural language. As a case study, we explore the task of learning to solve geometry problems using demonstrative solutions available in textbooks. We collect a new dataset of demonstrative geometry solutions from textbooks and explore approaches that learn to interpret these demonstrations as well as to use these interpretations to solve geometry problems. Our approaches show improvements over the best previously published system for solving geometry problems.- Anthology ID:
- S17-1029
- Volume:
- Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Nancy Ide, Aurélie Herbelot, Lluís Màrquez
- Venue:
- *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 251–261
- Language:
- URL:
- https://aclanthology.org/S17-1029
- DOI:
- 10.18653/v1/S17-1029
- Cite (ACL):
- Mrinmaya Sachan and Eric Xing. 2017. Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 251–261, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks (Sachan & Xing, *SEM 2017)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/S17-1029.pdf
- Data
- GeoS