On Causality Problem in Natural Language Processing Field

Authors

  • Altynay Yerkhassym Institute of Information and Computational Technologies
  • Alexandr A. Pak Kazakh-British Technical University
  • Iskander Akhmetov Kazakh-British Technical University
  • Amir Yelenov Kazakh-British Technical University
  • Alexander Gelbukh Instituto Politécnico Nacional

DOI:

https://doi.org/10.13053/cys-26-4-4434

Keywords:

Natural language processing, neural network, causality

Abstract

Natural language processing (NLP) field has been developing rapidly recently. This article consists mainly of literature review of the basic understanding and solving the causality problem in natural language processing field. Existing models may benefit from the concept of causality because conventional language models are brittle and spurious [10]. Incorporating the principle of causality could assist in resolving this issue. Since this issue affects seriously on the accuracy value of NLP methods and algorithms, it is worth paying attention to. Content of the article includes the authors who have been covered this topic and have made researches respecting mentioned problem, the results that have been achieved, the methods and approached that have been used and the data that was used in researches.

Author Biography

Alexander Gelbukh, Instituto Politécnico Nacional

Centro de Investigación en Computación

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Published

2022-12-25

Issue

Vol. 26 No. 4 (2022): 26(4) 2022

Section

Articles

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