User Modeling in Language Learning with Macaronic Texts
User Modeling in Language Learning with Macaronic Texts
复制标题
马卡罗语文本语言学习中的用户建模
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Jason Eisner
中科院分区：
文献类型：
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作者：
Adithya Renduchintala;Rebecca Knowles;Philipp Koehn;Jason Eisner
外语学习者在阅读过程中，可以借助同源词和语境线索习得新词汇。为了测量这种偶然的理解，我们设计了一个实验框架，涉及阅读混合语言的“macaronic”句子。使用通过Amazon Mechanical Turk收集的数据，我们训练一个图形模型，根据同源线索（与英语单词的编辑距离）、上下文线索（逐点互信息）和先前接触来模拟人类受试者对外语单词的理解。我们的模型在预测用户能够理解哪些单词方面做得很好，这应该有助于个性化外语教育的可理解文本的自动构建。
Foreign language learners can acquire new vocabulary by using cognate and context clues when reading. To measure such incidental comprehension, we devise an experimental framework that involves reading mixed-language “macaronic” sentences. Using data collected via Amazon Mechanical Turk, we train a graphical model to simulate a human subject’s comprehension of foreign words, based on cognate clues (edit distance to an English word), context clues (pointwise mutual information), and prior exposure. Our model does a reasonable job at predicting which words a user will be able to understand, which should facilitate the automatic construction of comprehensible text for personalized foreign language education.