research-article

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

Authors:

Cui WeiAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 18, Issue 4

Article No.: 37, Pages 1 - 16

https://doi.org/10.1145/3314939

Published: 09 May 2019 Publication History

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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Cited By

Liu SHe TLi JLi YKumar A(2022)An Effective Learning Evaluation Method Based on Text Data with Real-time Attribution - A Case Study for Mathematical Class with Students of Junior Middle School in ChinaACM Transactions on Asian and Low-Resource Language Information Processing10.1145/347436722:3(1-22)Online publication date: 16-Mar-2022
https://dl.acm.org/doi/10.1145/3474367
Qiao FZhu X(2020)Domain Intelligent Q&A user intention recognition based on keyword separation2020 International Conference on Culture-oriented Science & Technology (ICCST)10.1109/ICCST50977.2020.00049(224-229)Online publication date: Oct-2020
https://doi.org/10.1109/ICCST50977.2020.00049

Index Terms

A Neural Semantic Parser for Math Problems Incorporating Multi-Sentence Information
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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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

Editor:
Nianwen Xue
Brandeis University, Waltham, USA

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Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 May 2019

Accepted: 01 January 2019

Revised: 01 December 2018

Received: 01 December 2018

Published in TALLIP Volume 18, Issue 4

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Cited By

Liu SHe TLi JLi YKumar A(2022)An Effective Learning Evaluation Method Based on Text Data with Real-time Attribution - A Case Study for Mathematical Class with Students of Junior Middle School in ChinaACM Transactions on Asian and Low-Resource Language Information Processing10.1145/347436722:3(1-22)Online publication date: 16-Mar-2022
https://dl.acm.org/doi/10.1145/3474367
Qiao FZhu X(2020)Domain Intelligent Q&A user intention recognition based on keyword separation2020 International Conference on Culture-oriented Science & Technology (ICCST)10.1109/ICCST50977.2020.00049(224-229)Online publication date: Oct-2020
https://doi.org/10.1109/ICCST50977.2020.00049

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