Under the Microscope: Interpreting Readability Assessment Models for Filipino

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

3 Citations (SciVal)

Abstract

Readability assessment is the process of identifying the level of ease or difficulty of a certain piece of text for its intended audience. Approaches have evolved from the use of arithmetic formulas to more complex pattern-recognizing models trained using machine learning algorithms. While using these approaches provide competitive results, limited work is done on analyzing how linguistic variables affect model inference quantitatively. In this work, we dissect machine learning-based readability assessment models in Filipino by performing global and local model interpretation to understand the contributions of varying linguistic features and discuss its implications in the context of the Filipino language. Results show that using a model trained with top features from global interpretation obtained higher performance than the ones using features selected by Spearman correlation. Likewise, we also empirically observed local feature weight boundaries for discriminating reading difficulty at an extremely fine-grained level and their corresponding effects if values are perturbed.

Original languageEnglish
Title of host publicationProceedings of the 35th Pacific Asia Conference on Language, Information and Computation
PublisherAssociation for Computational Linguistics (ACL)
Pages1-10
Number of pages10
Publication statusPublished - 7 Nov 2021
Event35th Pacific Asia Conference on Language, Information and Computation, PACLIC 2021 - Shanghai, China
Duration: 5 Nov 20217 Nov 2021

Conference

Conference35th Pacific Asia Conference on Language, Information and Computation, PACLIC 2021
Country/TerritoryChina
CityShanghai
Period5/11/217/11/21

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable feedback and to Dr. Ani Almario of Adarna House for allowing us to use their children’s book dataset for this study. This work is also supported by the DOST National Research Council of the Philippines (NRCP).

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

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