SC-Ques: A Sentence Completion Question Dataset for English as a Second Language Learners
This provides a resource for ESL education and NLP research, but it is incremental as it applies existing methods to new data.
The authors tackled the problem of creating a dataset for sentence completion questions used in English as a Second Language learning, resulting in SC-Ques with 289,148 questions from real-world exams and a benchmark for training language models to solve them.
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). In this paper, we present a large-scale SC dataset, \textsc{SC-Ques}, which is made up of 289,148 ESL SC questions from real-world standardized English examinations. Furthermore, we build a comprehensive benchmark of automatically solving the SC questions by training the large-scale pre-trained language models on the proposed \textsc{SC-Ques} dataset. We conduct detailed analysis of the baseline models performance, limitations and trade-offs. The data and our code are available for research purposes from: \url{https://github.com/ai4ed/SC-Ques}.