Towards the assessment of free learner's utterances in CALL

Computer-Aided Language Learning (CALL) should play an important role in the modern training process because it provides easy accessible, adaptive and flexible ways of learning. This paper addresses the scenario of tutor-learner question answering and attempts to automate the free answers evaluation using the advantages of Natural Language Processing (NLP). Our current approach integrates shallow parsing for analysing the answers and allows the learners to enter various utterances to express themselves. However this variety does not impede the assessment of the student's answer as we check the utterances against the automatically generated scope of the correct answers. The usage of a "set of answers" instead of one predefined correct answer enables feedback elaboration that helps learners to understand better their knowledge gaps. Briefly, in this paper we show how the combination of shallow and deep semantic NLP techniques can improve the effectiveness of eLearning systems which support communication in free natural language and can make them more satisfactory and pleasant for their users.