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Two-Step Memory Networks for Deep Semantic Parsing of Geometry Word Problems

  • Ishadi JayasingheEmail author
  • Surangika Ranathunga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12011)

Abstract

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.

Keywords

Semantic parsing Memory networks Low-resource domains 

Notes

Acknowledgments

This research was funded by a Senate Research Committee (SRC) Grant of University of Moratuwa, Sri Lanka and LK Domain Registry, Sri Lanka.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of MoratuwaMoratuwaSri Lanka

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