Distractor Generation for Chinese Fill-in-the-blank Items

Shu Jiang; John Lee

doi:10.18653/v1/w17-5015

Distractor Generation for Chinese Fill-in-the-blank Items

Shu Jiang
, John Lee

Department of Linguistics and Translation

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

50 Citations (Scopus)

51 Downloads (CityUHK Scholars)

Abstract

This paper reports the first study on automatic generation of distractors for fill-in-the-blank items for learning Chinese vocabulary. We investigate the quality of distractors generated by a number of criteria, including part-of-speech, difficulty level, spelling, word co-occurrence and semantic similarity. Evaluations show that a semantic similarity measure, based on the word2vec model, yields distractors that are significantly more plausible than those generated by baseline methods. © 2017 Association for Computational Linguistics.

Original language	English
Title of host publication	Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
Place of Publication	Stroudsburg, PA
Publisher	Association for Computational Linguistics
Pages	143-148
ISBN (Print)	9781945626852
DOIs	https://doi.org/10.18653/v1/w17-5015
Publication status	Published - Sept 2017
Event	12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2017), held in conjunction with EMNLP 2017 - Copenhagen, Denmark Duration: 8 Sept 2017 → … https://aclanthology.org/volumes/W17-50/

Conference

Conference	12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2017), held in conjunction with EMNLP 2017
Place	Denmark
City	Copenhagen
Period	8/09/17 → …
Internet address	https://aclanthology.org/volumes/W17-50/

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This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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title = "Distractor Generation for Chinese Fill-in-the-blank Items",

abstract = "This paper reports the first study on automatic generation of distractors for fill-in-the-blank items for learning Chinese vocabulary. We investigate the quality of distractors generated by a number of criteria, including part-of-speech, difficulty level, spelling, word co-occurrence and semantic similarity. Evaluations show that a semantic similarity measure, based on the word2vec model, yields distractors that are significantly more plausible than those generated by baseline methods. {\textcopyright} 2017 Association for Computational Linguistics.",

author = "Shu Jiang and John Lee",

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language = "English",

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publisher = "Association for Computational Linguistics",

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note = "12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2017), held in conjunction with EMNLP 2017 ; Conference date: 08-09-2017",

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Jiang, S & Lee, J 2017, Distractor Generation for Chinese Fill-in-the-blank Items. in Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, Stroudsburg, PA, pp. 143-148, 12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2017), held in conjunction with EMNLP 2017, Copenhagen, Denmark, 8/09/17. https://doi.org/10.18653/v1/w17-5015

Distractor Generation for Chinese Fill-in-the-blank Items. / Jiang, Shu; Lee, John.
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications. Stroudsburg, PA: Association for Computational Linguistics, 2017. p. 143-148.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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Distractor Generation for Chinese Fill-in-the-blank Items

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