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

Adithya Renduchintala, Philipp Koehn, Jason Eisner


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.
Anthology ID:
D19-1679
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6438–6443
Language:
URL:
https://www.aclweb.org/anthology/D19-1679
DOI:
10.18653/v1/D19-1679
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http://aclanthology.lst.uni-saarland.de/D19-1679.pdf
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