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Proceedings of the Seventh International Conference on Mathematics and Computing pp 415–426Cite as

Identification of Relevant Quantities in Arithmetic Word Problems Using Siamese Neural Network

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1412))

Abstract

Presence of irrelevant sentences containing irrelevant quantities in Math Word Problems (MWPs) throws an immense challenge in formulating the final equation(s). Failure in identification of the relevant quantities with respect to the question asked in an MWP reduces the overall system performance. This paper demonstrates a novel deep learning-based approach using Siamese neural network to classify the relevant and irrelevant sentences towards identification of the relevant quantities in arithmetic MWPs. The proposed relevance classifier produced an accuracy of 91.0% on a dataset variant (RQDV) prepared from a Combined dataset of two standard datasets consisting of arithmetic word problems. Our relevance classifier may benefit any existing arithmetic MWP solvers in improving their system performance, and we test this by performing a case study on a prototype system in which the integration of our relevance classifier improved the accuracy of the MWP solver from 68.0 to 81.0% on the Combined dataset.

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Notes

  1. 1.

    https://sites.google.com/view/datasets-for-relevant-quantity/home.

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Author information

Authors and Affiliations

  1. XIM University, Bhubaneswar, OR, India

    Sourav Mandal & Arif Ahmed Sekh

  2. UiT The Arctic University of Norway, Tromsø, Norway

    Arif Ahmed Sekh

  3. Jadavpur University, Kolkata, Kolkata, India

    Sudip Kumar Naskar

Authors
  1. Sourav Mandal
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  2. Arif Ahmed Sekh
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  3. Sudip Kumar Naskar
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Corresponding author

Correspondence to Sourav Mandal .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Maulana Abul Kalam Azad University of Technology, Haringhata, India

    Debasis Giri

  2. The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. Department of Mathematics, Indian Institute of Technology Madras, Chennai, India

    Saminathan Ponnusamy

  4. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  5. Department of Computer Engineering, Ondokuz Mayis University, Atakum, Turkey

    Sedat Akleylek

  6. Department of Information Technology, Indian Institute of Engineering Science and Technology, West Bengal, India

    Santi Prasad Maity

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Mandal, S., Sekh, A.A., Naskar, S.K. (2022). Identification of Relevant Quantities in Arithmetic Word Problems Using Siamese Neural Network. In: Giri, D., Raymond Choo, KK., Ponnusamy, S., Meng, W., Akleylek, S., Prasad Maity, S. (eds) Proceedings of the Seventh International Conference on Mathematics and Computing . Advances in Intelligent Systems and Computing, vol 1412. Springer, Singapore. https://doi.org/10.1007/978-981-16-6890-6_31

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