Classification of Utterances Based on Multiple BLEU Scores for Translation-Game-Type CALL Systems

Abstract

SUMMARYThis paper proposes a classification method of secondlanguage-learner utterances for interactive computer-assisted language learning systems. This classification method uses three types of bilingual evaluation understudy (BLEU) scores as features for a classifier. The three BLEU scores are calculated in accordance with three subsets of a learner corpus divided according to the quality of utterances. For the purpose of overcoming the data-sparseness problem, this classification method uses the BLEU scores calculated using a mixture of word and part-of-speech (POS)-tag sequences converted from word sequences based on a POSreplacement rule according to which words are replaced with POS tags in n-grams. Experiments of classifying English utterances by Japanese demonstrated that the proposed classification method achieved classification accuracy of 78.2% which was 12.3 points higher than a baseline with one BLEU score.

Learning Effect of Implicit Learning in Joining-in-type Robot-assisted Language Learning System

“…As mentioned above, speech recognition of L2 speech is still difficult even for state-ofthe-art technology; however, classification of utterances is easier in comparison with speech recognition even if the input speech is L2 speech. The classification of utterances has been explored as detection of out-of-domain utterances in the research community of speech recognition [36], and two kinds of language models, a general language model and a model specifically designed for utterances in a domain, are generally used to classify utterances into two categories based on a comparison of acoustic likelihood between outputs from speech recognizers using each language model [17]. In the case of detecting whether speech is constructed based on the appropriate grammatical pattern, a phonetic typewriter model is used as a general language model, and a language model may be designed to consist of a finite state automaton model concatenating words representing the appropriate grammatical pattern sandwiched between two garbage models.…”