Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models
This work addresses a practical problem for ESL learners and educators by providing an automated tool for sentence completion questions, though it is incremental as it applies existing pre-trained models to a specific domain.
The paper tackled the problem of automatically solving English as a Second Language (ESL) sentence completion questions by proposing a neural framework using pre-trained language models, achieving superior prediction accuracy on a real-world K-12 dataset.
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 trade-off analysis to discuss the practical issues when deploying it in real-life scenarios. To encourage reproducible results, we make our code publicly available at \url{https://github.com/AIED2021/ESL-SentenceCompletion}.