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A Neural Semantic Parser for Math Problems Incorporating Multi-Sentence Information

Published:09 May 2019Publication History
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Abstract

In this article, we study the problem of parsing a math problem into logical forms. It is an essential pre-processing step for automatically solving math problems. Most of the existing studies about semantic parsing mainly focused on the single-sentence level. However, for parsing math problems, we need to take the information of multiple sentences into consideration. To achieve the task, we formulate the task as a machine translation problem and extend the sequence-to-sequence model with a novel two-encoder architecture and a word-level selective mechanism. For training and evaluating the proposed method, we construct a large-scale dataset. Experimental results show that the proposed two-encoder architecture and word-level selective mechanism could bring significant improvement. The proposed method can achieve better performance than the state-of-the-art methods.

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    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 18, Issue 4
      December 2019
      305 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3327969
      Issue’s Table of Contents

      Copyright © 2019 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 9 May 2019
      • Accepted: 1 January 2019
      • Revised: 1 December 2018
      • Received: 1 December 2018
      Published in tallip Volume 18, Issue 4

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