Publications

Abstract

Students who are learning to write in a foreign language, often want feedback on the grammatical quality of the sentences they produce. The usual NLP approach to this problem is based on parsing student-generated text. Here, we propose a generation-based ap- proach aiming at preventing errors ("scaffolding"). In our ICALL system, the student constructs sentences by composing syntactic trees out of lexically anchored "treelets" via a graphical drag & drop user interface. A natural-language generator computes all possible grammatically well-formed sentences entailed by the student-composed tree. It provides positive feedback if the student-composed tree belongs to the well-formed set, and negative feedback otherwise. If so requested by the student, it can substantiate the positive or negative feedback based on a comparison between the student-composed tree and its own trees (informative feedback on demand). In case of negative feedback, the system refuses to build the structure attempted by the student. Frequently occurring errors are handled in terms of "malrules." The system we describe is a prototype (implemented in JAVA and C++) which can be parameterized with respect to L1 and L2, the size of the lexicon, and the level of detail of the visually presented grammatical structures.

Abstract

Two experimental results often reported in support of perceptual interpretations concerning the influence of set on perception are critically examined: (a) the relation between word frequency and recognition threshold, and (b) the so-called compromise reactions between set and stimulus, Alter elimination of certain methodological artifacts (e.g., introduction of a temporal forced-choice method instead of the ascending-limits method), both phenomena disappear; the influence of set on perception appears to be wholly a matter of response bias.