Constructive learning of translations based on dictionaries

Abstract

Learning a translation based on a dictionary is to extract a binary relation over strings from given examples based on information supplied by the dictionary. In this paper, we introduce a restricted elementary formal system called a regular TEFS to formalize translations and dictionaries. Then, we propose a learning algorithm that identifies a translation defined by a regular TEFS from positive and negative examples. The main advantage of the learning algorithm is constructive, that is, the produced hypothesis reflects the examples directly. The learning algorithm generates the most specific clauses from examples by referring to a dictionary, generalizes these clauses, and then removes too strong clauses from them. As a result, the algorithm can learn translations over context-free languages.