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

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

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics
Pages143-148
ISBN (print)9781945626852
Publication statusPublished - Sept 2017

Conference

Title12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2017), held in conjunction with EMNLP 2017
PlaceDenmark
CityCopenhagen
Period8 September 2017

Link(s)

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.

Research Area(s)

Citation Format(s)

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 WorksRGC 32 - Refereed conference paper (with host publication)peer-review