Abstract:

We have developed an entirely new template-automaton-based knowledge database system for an interactive intelligent language tutoring system (ILTS) for Japanese-English translation whereby model translations as well as a taxonomy of bugs extracted from ill formed translations typical of nonnative learners are collected. Unlike conventional rule-based systems whose complicated solution search procedure and labor-intensive processing have led to so-called knowledge engineer bottlenecks of the expert systems, the new dynamic programming-based heaviest common sequence (HCS) matching algorithm is both efficient and robust in which error diagnosis is implemented by selecting, from among many candidates' paths in the system template, a path having an HCS of a highest similarity with a student's free-format translation input. This best matched path to the given ill formed sentence is used to provide contingent feedback messages. We have laid down a theoretical framework for the global HCS matching algorithm which is applied to the dual form of acyclic weighted digraphs by topologically sorting the template automaton structured according to augmented transition networks. An extensive evaluation test of the diagnostic engine has ensured the validity, efficiency, and robustness of the algorithm in providing error-contingent feedback to a wide spectrum of learners even with different educational backgrounds.

Author: Liang Chen, Naoyuki Tokuda