Machine Translation: A Knowledge-Based Approach
Machine Translation: A Knowledge-Based Approach
Inside Case-Based Reasoning
A memory-based approach to learning shallow natural language patterns
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A matching technique in Example-Based Machine Translation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
A similarity-driven transfer system
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
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Interactive Hybrid Strategies Machine Translation (IHSMT) system has just been designed to solve the translation problems. It forms a nice interdependent cooperation relation between human and machine by interaction. The system achieves hybrid strategy translation by synthesizing the rule-based reasoning and case-based reasoning, and therefore overcomes the demerits of single strategy. This paper has done some work on learning mechanism of this system and proposes a learning model of human-machine tracking and memorizing (HMTM). This model can store the information of human-machine interaction into memory base as case of machine learning, and then gradually accumulate knowledge to improve the intelligence of MT system.