Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning

Yo Ehara, Yusuke Miyao, Hidekazu Oiwa, Issei Sato, Hiroshi Nakagawa

Abstract: Predicting vocabulary of second language learners is essential to support their language learning; however, because of the large size of language vocabularies, we cannot collect information on the entire vocabulary. For practical measurements, we need to sample a small portion of words from the entire vocabulary and predict the rest of the words. In this study, we propose a novel framework for this sampling method. Current methods rely on simple heuristic techniques involving inflexible manual tuning by educational experts. We formalize these heuristic techniques as a graph-based non-interactive active learning method as applied to a special graph. We show that by extending the graph, we can support additional functionality such as incorporating domain specificity and sampling from multiple corpora. In our experiments, we show that our extended methods outperform other methods in terms of vocabulary prediction accuracy when the number of samples is small.