Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks

Mrinmaya Sachan, Eric Xing


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
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
251–261
Language:
URL:
https://www.aclweb.org/anthology/S17-1029
DOI:
10.18653/v1/S17-1029
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-1029.pdf