论文标题
语言距离与一个国家的英语能力的关系
Relationship of the language distance to English ability of a country
论文作者
论文摘要
语言差异是阻碍获得第二语言技能的因素之一。在本文中，我们介绍了一种新颖的解决方案，该解决方案利用深神经网络的强度来测量语言之间的语义差异，这些语言基于其单词分布在多语种预训练的语言模型（例如BERT）的嵌入空间中。然后，我们从经验上研究了拟议语言距离（SLD）在解释国家能力的一致变化方面的有效性，这是由于它们在基于Internet的英语测试中作为外语（TOEFL IBT）的表现所代表的。实验结果表明，语言距离表明对一个国家的平均英语能力的负面影响。有趣的是，这种效果对口语和写作的效果更为重要，这与语言学习的生产性方面有关。此外，我们为将来的研究方向提供具体建议。
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.
