Relationship of the language distance to English ability of a country
This work addresses the challenge of understanding how language differences affect English learning outcomes for countries, but it is incremental as it applies an existing method to a new domain.
The authors tackled the problem of measuring language distance using deep neural networks to predict English proficiency, finding that semantic language distance negatively impacts a country's average TOEFL iBT scores, with stronger effects on speaking and writing subskills.
Language difference is one of the factors that hinder the acquisition of second language skills. In this article, we introduce a novel solution that leverages the strength of deep neural networks to measure the semantic dissimilarity between languages based on their word distributions in the embedding space of the multilingual pre-trained language model (e.g.,BERT). Then, we empirically examine the effectiveness of the proposed semantic language distance (SLD) in explaining the consistent variation in English ability of countries, which is proxied by their performance in the Internet-Based Test of English as Foreign Language (TOEFL iBT). The experimental results show that the language distance demonstrates negative influence on a country's average English ability. Interestingly, the effect is more significant on speaking and writing subskills, which pertain to the productive aspects of language learning. Besides, we provide specific recommendations for future research directions.