Natural Language Generation: Complexities and Techniques,
MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
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This paper examines the nature of generation systems today, the problems they have been designed to deal with, their strengths and their weakness. Its goals to give the MT community a sense of what has been accomplished, and indirectly to show where MT researchers could consider adopting or adapting some of the AI work. This work on generation need not be done by AI people alone MT can, for example, contribute to AI research on the planning-level by sharpening our collective understanding of the carrying capacity of the different parts of a language through cross-language comparisons that try to fit the ideas carried by the linguistic devices of a source language into the alternative devices of a target language. At lower levels, MT as a task can provide more linguistically demanding sources for generation than most any of todays expert systems. At the same time it is clear that generation is done for very different reasons in two camps. The AI context is more like that of people dealing with each other in normal life--of which translation is not a customary part. Nevertheless, translation is a normal human capacity, and a considered comparison of the generation process in both contexts should tell us more about the nature of generation as a module within the human mind than could either by itself.