- Title
- Solving ESL sentence completion questions via pre-trained neural language models
- Creator
- Liu, Qiongqiong; Liu, Tianqiao; Zhao, Jiafu; Fang, Qiang; Ding, Wenbiao; Wu, Zhongqin; Xia, Feng; Tang, Jiliang; Liu, Zitao
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/188583
- Identifier
- vital:17316
- Identifier
-
https://doi.org/10.1007/978-3-030-78270-2_46
- Identifier
- ISBN:0302-9743 (ISSN); 9783030782696 (ISBN)
- Abstract
- Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options. SC questions are widely used for students learning English as a Second Language (ESL) and building computational approaches to automatically solve such questions is beneficial to language learners. In this work, we propose a neural framework to solve SC questions in English examinations by utilizing pre-trained language models. We conduct extensive experiments on a real-world K-12 ESL SC question dataset and the results demonstrate the superiority of our model in terms of prediction accuracy. Furthermore, we run precision-recall tradeoff analysis to discuss the practical issues when deploying it in real-life scenarios. To encourage reproducible results, we make our code publicly available at https://github.com/AIED2021/ESL-SentenceCompletion. © Springer Nature Switzerland AG 2021.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Virtual, Online, 14-18 June 2021, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12749 LNAI, p. 256-261
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © Springer Nature Switzerland AG 2021
- Rights
- Open Access
- Subject
- Neural networks; Pre-trained language model; Sentence completion
- Full Text
- Reviewed
- Funder
- National Key R&D Program of China, under Grant No. 2020AAA0104500 Beijing Nova Program (Z201100006820068) from Beijing Municipal Science & Technology Commission
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