Sugimoto N. (1998) Learnability of Translations from Positive Examples. In: Richter M.M., Smith C.H., Wiehagen R., Zeugmann T. (eds) Algorithmic Learning Theory. ALT 1998. Lecture Notes in Computer Science, vol 1501. Springer, Berlin, Heidelberg
One of the most important issues in machine translations is deducing unknown rules from pairs of input-output sentences. Since the translations are expressed by elementary formal systems (EFS’s, for short), we formalize learning translations as the process of guessing an unknown EFS from pairs of input-output sentences. In this paper, we propose a class of EFS’s called linearly-moded EFS’s by introducing local variables and linear predicate inequalities based on mode information, which can express translations of context-sensitive languages. We show that, for a given input sentence, the set of all output sentences is finite and computable in a translation defined by a linearly-moded EFS. Finally, we show that the class of translations defined by linearly-moded EFS’s is learnable under the condition that the number of clauses in an EFS and the length of the clause are bounded by some constant.