Analyzing learner language: towards a flexible natural language processing architecture for intelligent language tutors

Intelligent language tutoring systems (ILTS) typically analyze learner input to diagnose learner language properties and provide individualized feedback. Despite a long history of ILTS research, such systems are virtually absent from real-life foreign language teaching (FLT). Taking a step toward more closely linking ILTS research to real-life FLT, in this article we investigate the connection between FLT activity design and the system architecture of an ILT system. We argue that a demand-driven, annotation-based natural language processing (NLP) architecture is well-suited to handle the demands posed by the heterogeneous learner input which results when supporting a wider range of FLT activity types. We illustrate how the unstructured information management architecture (UIMA) can be used in an ILTS, thereby connecting the specific needs of activities in foreign language teaching to the current research and development of NLP architectures in general. Making the conceptual issues concrete, we discuss the design and realization of a UIMA-based reimplementation of the NLP in the TAGARELA system, an intelligent web-based tutoring system supporting the teaching and learning of Portuguese.