Introduction to the special issue on natural language generation
Robert Dale, Donia Scott, Barbara Di Eugenio · 1998
There are two sides to natural language processing. On the one hand, work in natural language understanding is concerned with the mapping from some surface representation of linguistic material expressed as speech or text--to an underlying representation of the meaning carried by that surface representation. But there is also the question of how one maps from some underlying representation of meaning into text or speech: this is the domain of natural language generation. Whether our end-goal is the construction of artifacts that use natural languages intelligently, the formal characterization of phenomena in human languages, or the computational modeling of the human language processing mechanism, we cannot ignore the fact that language is both spoken (or written) and heard (or read). Both are equally large and important problems, but the literature contains much less work on natural language generation (NLG) than it does on natural language understanding (NLU). There are many reasons why this might be so, although clearly an important one is that researchers in natural language understanding in some sense start out with a more well-defined task: the input is known, and there is a lot of it around. This is not the case in natural language generation: there, it is the desired output that is known, but the input is an unknown; and while the world is awash with text waiting to be processed, there are fewer instances of what we might consider appropriate inputs for the process of natural language generation. For researchers in the field, this highlights the fundamental question that always has to be asked: What do we generate from? Despite this problem, the natural language generation community is a thriving one, with a research base that has been developing steadily--although perhaps at a slower pace because of the smaller size of the community--for just as long as work in natural language understanding. It should not be forgotten that much of NLP has its origins in the early work on machine translation in the 1950s; and that to carry out machine translation, one has to not only analyze existing texts but also to generate new ones. The early machine translation experiments, however, did not recognize the problems that give modern work in NLG its particular character. The first significant pieces of work in the field appeared during the 1970s; in particular, Goldman's work on the problem of lexicalizing underlying conceptual material (Goldman 1974) and