Text Readability Assessment for Second Language Learners
This work addresses text readability for second language learners, but it is incremental as it adapts existing methods to a new domain.
The paper tackled the problem of readability assessment for second language learners by addressing data scarcity through a new dataset and adaptation methods, achieving an accuracy of 0.797 and PCC of 0.938.
This paper addresses the task of readability assessment for the texts aimed at second language (L2) learners. One of the major challenges in this task is the lack of significantly sized level-annotated data. For the present work, we collected a dataset of CEFR-graded texts tailored for learners of English as an L2 and investigated text readability assessment for both native and L2 learners. We applied a generalization method to adapt models trained on larger native corpora to estimate text readability for learners, and explored domain adaptation and self-learning techniques to make use of the native data to improve system performance on the limited L2 data. In our experiments, the best performing model for readability on learner texts achieves an accuracy of 0.797 and PCC of $0.938$.