ALBS: A daptive language learning by l everaging B ERT and s emantic technologies for customized lexical proficiency

Abstract

Incorporating classic literature as a part of language learning is not only engaging but also interesting for English as a Foreign Language (EFL) learners. It immerses them in the narrative, sparking their curiosity and motivation to keep reading. However, the rich vocabulary found in these classics may pose a significant challenge and surpass the proficiency levels of learners. This study proposes a novel mechanism called Adaptive Language Learning by Leveraging BERT and Semantic Technologies (ALBS) to address this issue. The proposed ALBS aims to enhance customized lexical proficiency for EFL learners. It replaces challenging vocabulary with words better aligned with learners’ lexical capabilities while preserving the original semantic meaning and syntactic fluency. It facilitates the recommendation of classic literature to EFL learners at varying vocabulary proficiency levels, enabling tailored learning experiences and suitable lexical competency, which enhances learner motivation. The proposed ALBS is structured into three distinct phases to achieve this goal. In the first phase, an L-BERT model is trained using Natural Language Processing (NLP) techniques, aiming to identify the level of each word from the input sentence. Then the distributions of the vocabulary proficiency levels of both classic literature and the learner are mapped out in percentages through L-BERT. Finally, in the word replacement phase, the three criteria of fluency, semantics, and the Common European Framework of Reference for Languages (CEFR) word level are integrated to select the most suitable word from the list as a replacement for the target word. The results indicate that the proposed ALBS outperforms the existing mechanisms in terms of precision, recall, and F1-score.