  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}.
