Integrating parallel analysis modules to evaluate the meaning of answers to reading comprehension questions

Contextualised, meaning-based interaction in the foreign language is widely recognised as crucial for second language acquisition. Correspondingly, current exercises in foreign language teaching generally require students to manipulate both form and meaning. For intelligent language tutoring systems to support such activities, they thus must be able to evaluate the appropriateness of the meaning of a learner response for a given exercise. We discuss such a content-assessment approach, focusing on reading comprehension exercises. We pursue the idea that a range of simultaneously available representations at different levels of complexity and linguistic abstraction provide a good empirical basis for content assessment. We show how an annotation-based NLP architecture implementing this idea can be realised and that it successfully performs on a corpus of authentic learner answers to reading comprehension questions. To support comparison and sustainable development on content assessment, we also define a general exchange format for such exercise data.