  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 $0.938$.
