Towards a Better Learning of Near-Synonyms: Automatically Suggesting Example Sentences via Fill in the Blank

Language learners are confused by near-synonyms and often look for answers from the Web. However, there is little to aid them in sorting through the overwhelming load of information that is offered. In this paper, we propose a new research problem: suggesting example sentences for learning word distinctions. We focus on near-synonyms as the first step. Two kinds of one-class classifiers, the GMM and BiLSTM models, are used to solve fill-in-the-blank (FITB) questions and further to select example sentences which best differentiate groups of near-synonyms. Experiments are conducted on both an open benchmark and a private dataset for the FITB task. Experiments show that the proposed approach yields an accuracy of 73.05% and 83.59% respectively, comparable to state-of-the-art multi-class classifiers. Learner study further shows the results of the example sentence suggestion by the learning effectiveness and demonstrates the proposed model indeed is more effective in learning near-synonyms compared to the resource-based models.