Two-Step Memory Networks for Deep Semantic Parsing of Geometry Word Problems
Semantic parsing of geometry word problems (GWPs) is the first step towards automated geometry problem solvers. Existing systems for this task heavily depend on language-specific NLP tools, and use hard-coded parsing rules. Moreover, these systems produce a static set of facts and record low precision scores. In this paper, we present the two-step memory network, a novel neural network architecture for deep semantic parsing of GWPs. Our model is language independent and optimized for low-resource domains. Without using any language-specific NLP tools, our system performs as good as existing systems. We also introduce on-demand fact extraction, where a solver can query the model about entities during the solving stage that alleviates the problem of imperfect recalls.
KeywordsSemantic parsing Memory networks Low-resource domains
This research was funded by a Senate Research Committee (SRC) Grant of University of Moratuwa, Sri Lanka and LK Domain Registry, Sri Lanka.
- 2.Sachan, M., Xing, E.: 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), pp. 251–261 (2017)Google Scholar
- 3.Seo, M., et al.: Solving geometry problems: combining text and diagram interpretation. In: Proceedings of EMNLP 2015, pp. 1466–1476 (2015)Google Scholar
- 4.Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of EMNLP 2013, pp. 1631–1642 (2013)Google Scholar
- 5.Sukhbaatar, S., et al.: End-to-end memory networks. In: NIPS, pp. 2440–2448 (2015)Google Scholar
- 6.Weston, J., et al.: Memory networks. arXiv preprint arXiv:1410.3916 (2014)
- 7.Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048–2057 (2015)Google Scholar