A revision learner to acquire verb selection rules from human-made rules and examples

Abstract

This paper proposes a learning method that automatically acquires English verb selection rules for machine translation using a machine learning technique. When learning from real translation examples alone, many examples are needed to achieve good translation quality. It is, however, difficult to gather a sufficiently large number of real translation examples. The main causes are verbs of low frequency and the frequent usage of the same sentences. To resolve this problem, the proposed method learns English verb selection rules from hand-made translation rules and a small number of real translation examples. The proposed method has two steps: generating artificial examples from the hand-made rules, and then putting those artificial examples and real examples into an internal learner as the training set. The internal learner outputs the final rules with improved verb selection accuracy. The most notable feature of the proposed learner is that any attribute-type learning algorithm can be adopted as the internal learner. To evaluate the validity of the proposed learner, English verb selection rules of NTT's Japanese-English Machine Translation System ALT-J/E are experimentally learned from hand-made rules and real examples. The resultant rules have better accuracy than either those constructed from the real examples or those that are hand-made.