论文标题
Mulve，一个多语言词汇评估数据集
MuLVE, A Multi-Language Vocabulary Evaluation Data Set
论文作者
论文摘要
词汇学习对于外语学习至关重要。正确且充分的反馈对于成功和令人满意的词汇培训至关重要。但是，许多词汇和语言评估系统在简单的规则上执行，并且不考虑现实生活中的用户学习数据。这项工作介绍了多语言词汇评估数据集（MULVE），该数据集由词汇卡和现实生活中的用户答案组成，标记为用户答案是正确还是不正确。数据源是从第6阶段词汇教练中学习数据。数据集包含德语和英语，西班牙语和法语作为目标语言的词汇问题，并且在预处理和重复数据上有四种不同的变体。我们尝试使用拟议的Mulve数据集对词汇评估的下游任务进行微调预训练的BERT语言模型。结果提供了> 95.5精度和F2得分的出色结果。数据集可在欧洲语言网格上找到。
Vocabulary learning is vital to foreign language learning. Correct and adequate feedback is essential to successful and satisfying vocabulary training. However, many vocabulary and language evaluation systems perform on simple rules and do not account for real-life user learning data. This work introduces Multi-Language Vocabulary Evaluation Data Set (MuLVE), a data set consisting of vocabulary cards and real-life user answers, labeled indicating whether the user answer is correct or incorrect. The data source is user learning data from the Phase6 vocabulary trainer. The data set contains vocabulary questions in German and English, Spanish, and French as target language and is available in four different variations regarding pre-processing and deduplication. We experiment to fine-tune pre-trained BERT language models on the downstream task of vocabulary evaluation with the proposed MuLVE data set. The results provide outstanding results of > 95.5 accuracy and F2-score. The data set is available on the European Language Grid.
