A comprehensive word difficulty index for L2 listening
DOI:
https://doi.org/10.36505/ExLing-2018/09/0019/000352Keywords:
Word listening difficulty score, partial and synchronized captionAbstract
Word difficulty in listening tasks is considered challenging to assess because of its high subjectivity, high dimensionality, and low generalizability. We propose a word listening difficulty score formulated as a linear combination of several complementary features. A dataset of expert-annotated, partial, and synchronized captions for TED Talks was prepared for a target language proficiency level, in which only the difficult words are displayed. A linear Support Vector Machine (SVM) was trained on this dataset, and the learned parameters of the model were transferred to the proposed score. This data-driven score demonstrates higher accuracy on the annotated dataset and facilitates easy model and feature expansion.
References
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Kotani, K., & Yoshimi, T. 2017. Effectiveness of linguistic and learner features for listenability measurement using a decision tree classifier. *The Journal of Information and Systems in Education*, 16(1), 7-11.
Mirzaei, M.S., Meshgi, K., Akita, Y., & Kawahara, T. 2017. Partial and synchronized captioning: Development of a tech-assisted listening tool for foreign language learners. *ReCALL*, 29(2), 178-199.
Yoon, S.Y., Cho, Y., & Napolitano, D. 2016. Spoken text difficulty estimation using linguistic features. In *Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2016)*, 267-276. San Diego, CA: Association for Computational Linguistics.
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Copyright (c) 2018 Kourosh Meshgi, Maryam Sadat Mirzaei (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
