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Estimation Method of L2 Learners' Second Language Ability by using Features in Conversation

We are conducting a research to train second language(L2) learners's second language ability by utilizing chat system. The main problem of existing chat systems is that it is not possible to chat with learners to adapt their second language level. In this research, in order to add a function to an existing chat system we need to measure the learner's second language level. So, to extract learners' second language capability, we propose a method to predict the language examination score of learners from chat context. This research investigates, first whether the number of utterances, number of sentences, word tokens and word types per utterance of chat context are correlated with second language examination score. Second, we build a predicting model to see the relationship between the chat context and second language examination score. As feature values of regression model for predicting the language examination score, we use variables chat time, sentence time, word token and word type. Also the unnatural sentence structure as a variable. For evaluation we use the root mean square error to check the results of prediction model, we use this model with Japanese and English chat and compare the results. We show how this chat context data is affecting the second language examination score and discuss strategies for future enhancements.