Effectiveness of Linguistic and Learner Features to Listenability Measurement Using a Decision Tree Classifier

Katsunori Kotani, Takehiko Yoshimi


Abstract
In learning Asian languages, learners encounter the problem of character types that are different from those in their first language, for instance, between Chinese characters and the Latin alphabet. This problem also affects listening because learners reconstruct letters from speech sounds. Hence, special attention should be paid to listening practice for learners of Asian languages. However, to our knowledge, few studies have evaluated the ease of listening comprehension (listenability) in Asian languages. Therefore, as a pilot study of listenability in Asian languages, we developed a measurement method for learners of English in order to examine the discriminability of linguistic and learner features. The results showed that the accuracy of our method outperformed a simple majority vote, which suggests that a combination of linguistic and learner features should be used to measure listenability in Asian languages as well as in English.
Anthology ID:
W16-4902
Volume:
Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Hsin-Hsi Chen, Yuen-Hsien Tseng, Vincent Ng, Xiaofei Lu
Venue:
NLP-TEA
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
6–10
Language:
URL:
https://aclanthology.org/W16-4902
DOI:
Bibkey:
Cite (ACL):
Katsunori Kotani and Takehiko Yoshimi. 2016. Effectiveness of Linguistic and Learner Features to Listenability Measurement Using a Decision Tree Classifier. In Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016), pages 6–10, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Effectiveness of Linguistic and Learner Features to Listenability Measurement Using a Decision Tree Classifier (Kotani & Yoshimi, NLP-TEA 2016)
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