Simple Construction of Mixed-Language Texts for Vocabulary Learning
Simple Construction of Mixed-Language Texts for Vocabulary Learning
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DOI：
10.18653/v1/w19-4439
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Adithya Renduchintala;Philipp Koehn;Jason Eisner
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文献类型：
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作者：
Adithya Renduchintala;Philipp Koehn;Jason Eisner
我们提出了一个机器外语教师，需要在学生的母语写的文件，并检测的情况下，它可以取代他们的外国光泽的话，新的外国词汇可以简单地通过阅读所产生的混合语言文本学习。我们表明，有可能设计这样一个机器教师没有任何监督数据（人类）的学生。我们通过修改完形填空的语言模型来逐步学习新的词汇项目，并使用这个语言模型作为真实的学生的猜词和学习能力的代理。我们的机器外语老师通过咨询这个语言模型来决定替换哪个子集的单词。我们通过对Amazon Mechanical Turk（MTurk）的研究评估了我们的学生代理语言模型的三种变体。我们发现，MTurk的“学生”能够猜测的机器老师介绍的外国词的含义，具有高精度的功能词，以及内容词在两个三个模型。此外，我们发现学生在完成阅读文件后，能够保留他们对外来词的知识。
We present a machine foreign-language teacher that takes documents written in a student’s native language and detects situations where it can replace words with their foreign glosses such that new foreign vocabulary can be learned simply through reading the resulting mixed-language text. We show that it is possible to design such a machine teacher without any supervised data from (human) students. We accomplish this by modifying a cloze language model to incrementally learn new vocabulary items, and use this language model as a proxy for the word guessing and learning ability of real students. Our machine foreign-language teacher decides which subset of words to replace by consulting this language model. We evaluate three variants of our student proxy language models through a study on Amazon Mechanical Turk (MTurk). We find that MTurk “students” were able to guess the meanings of foreign words introduced by the machine teacher with high accuracy for both function words as well as content words in two out of the three models. In addition, we show that students are able to retain their knowledge about the foreign words after they finish reading the document.