Generation Systems Should Choose Their Words

Almost all current natural language generation (NLG) systems, as contrasted with current NL understanding (NLU) systems, have one somewhat surprising property in common: Most current NLG systems don't use words at all. Such systems operate by incrementally specifying fragments of linguistic structure in a top-down fashion, typically inserting specific lexical items only when the frontier of the structure is encountered. In some important sense, these systems have no real knowledge of lexical semantics and only rarely make lexical choices. Instead they choose between one tree fragment or another, only rarely able to see the leaves for the trees. Somehow the fact that particular words have particular meanings is incidental to the operation of these systems; they use fragments of linguistic structure which eventually have words as their frontiers, but they have little or no explicit knowledge of these words and what they mean. At best, these systems assume that each conceptual primitive corresponds to a particular unique lexical item or phrase, trivializing the problem of lexical choice to one of table lookup, and trivializing the problem of lexical semantics to the claim that the meaning of the word can be represented by the same word in upper case, more or less. While this practice may suffice for generation systems for narrow application domains, it is most certainly wrong from a cognitive point of view.