Spelling-Aware Construction of Macaronic Texts for Teaching Foreign-Language Vocabulary

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 6438–6443, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 6438 Spelling-Aware Construction of Macaronic Texts for Teaching Foreign-Language Vocabulary Adithya Renduchintala and Philipp Koehn and Jason Eisner Center for Language and Speech Processing Johns Hopkins University {adi.r,phi}@jhu.edu jason@cs.jhu.edu Abstract We present a machine foreign-language teacher that modifies text in a student’s native language (L1) by replacing some word tokens with glosses in a foreign language (L2), in such a way that the student can acquire L2 vocabulary simply by reading the resulting macaronic text. The machine teacher uses no supervised data from human students. Instead, to guide the machine teacher’s choice of which words to replace, we equip a cloze language model with a training procedure that can incrementally learn representations for novel words, and use this model as a proxy for the word guessing and learning ability of real human students. We use Mechanical Turk to evaluate two variants of the student model: (i) one that generates a representation for a novel word using only surrounding context and (ii) an extension that also uses the spelling of the novel word. 1 Introduction Reading plays an important role in building our native language (L1) vocabulary (Nation, 2001). While some novel words might require the assis- tance of a dictionary, a large portion are acquired through incidental learning – where a reader, exposed to a novel word, tries to infer its meaning using clues from the surrounding context and spelling (Krashen, 1989). An initial “rough” under- standing of a novel word might suffice for the reader to continue reading, with subsequent exposures refining their understanding of the novel word. Our goal is to design a machine teacher that uses a human reader’s incidental learning ability to teach foreign language (L2) vocabulary. The machine teacher’s modus operandi is to replace L1 words with their L2 glosses, which results in a macaronic document that mixes two languages in an effort to ease the human reader into understanding the L2. While some of our prior work (Renduchintala et al., 2016b,a) considered incorporating other features of the L2 such as word order and fixed phrases, in this paper we only consider simple lexical substitutions. Our hope is that such a system can augment tradi- tional foreign language instruction. As an example, consider a native speaker of English (learning German) presented with the following sentence: Der Nile is a Fluss in Afrika. With a little effort, one would hope the student could infer the meaning of the German words because there is sufficient contextual information and spelling information for the cognate Afrika. In our previous papers on foreign language teaching (Renduchintala et al., 2016b; Knowles et al., 2016; Renduchintala et al., 2017), we focused on fitting detailed models of students’ learning when the instructional stimuli (macaronic or otherwise) were chosen by a simple random or heuristic teaching policy. In the …

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