| this paper presents three trainable systems for surface natural language generation (nlg). surface nlg, for our purposes, consists of generating a grammatical natural language phrase that expresses the meaning of an input semantic representation. the systems take a "corpus-based" or "machinelearning" approach to surface nlg, and learn to generate phrases from semantic input by statistically analyzing examples of phrases and their corresponding semantic representations. the determination of the content in the semantic representation, or "deep" generation, is not discussed here. instead, the systems assume that the input semantic representation is fixed and only deal with how to express it in natural language. this paper discusses previous approaches to surface nlg, and introduces three trainable systems for surface nlg, called nlg1, nlg2, and nlg3. quantitative evaluation of experiments in the air travel domain will also be discussed.this paper presents three trainable systems for surface natural language generation (nlg). quantitative evaluation of experiments in the air travel domain will also be discussed. this paper presents the first systems (known to the author) that use a statistical learning approach to produce natural language text directly from a semantic representation. we conjecture that nlg2 and nlg3 should work in other domains which have a complexity similar to air travel, as well as available annotated data. the nlg2 and nlg3 systems automatically attempt to generalize from the knowledge inherent in the training corpus of templates, so that they can generate templates for novel attribute sets. in contrast, (langkilde and knight, 1998) uses corpus-derived statistical knowledge to rank plausible hypotheses from a grammarbased surface generation component. templates are the easiest way to implement surface nlg. this limitation can be overcome by using features on values, so that nlg2 and nlg3 might discover — to use a hypothetical example — that "flights leaving $city-fr" is preferred over "flights from $city-fr" when $city-fr is a particular value, such as "miami". our current approach has the limitation that it ignores the values of attributes, even though they might strongly influence the word order and word choice. |