The order of prenominal adjectival modifiers in English is governed by complex and difficult to describe constraints which straddle the boundary between competence and performance. This paper describes and compares a number of statistical and machine learning techniques for ordering sequences of adjectives in the context of a natural language generation system. The question of robustness is a perennial problem for parsing systems. In order to be useful, a parser must be able to accept a wide range of input types, and must be able to gracefully deal with dysfluencies, false starts, and other ungrammatical input. In natural language generation, on the other hand, robustness is not an issue in the same way. While a tactical generator must be able to deal with a wide range of semantic inputs, it only needs to produce grammatical strings, and the grammar writer can select in advance which construction types will be considered grammatical. However, it is important that a generator not produce strings which are strictly speaking grammatical but for some reason unusual. This is a particular problem for dialog systems which use the same grammar for both parsing and generation. The looseness required for robust parsing is in direct opposition to the tightness needed for high quality generation. One area where this tension shows itself clearly is in the order of prenominal modifiers in English. In principle, prenominal adjectives can, depending on context, occur in almost any order: the large red American car ??the American red large car *car American red the large Some orders are more marked than others, but none are strictly speaking ungrammatical. So, the grammar should not put any strong constraints on adjective order. For a generation system, however, it is important that sequences of adjectives be produced in the 'correct' order. Any other order will at best sound odd and at worst convey an unintended meaning. Unfortunately, while there are rules of thumb for ordering adjectives, none lend themselves to a computational implementation. For example, adjectives denoting size do tend to precede adjectives denoting color. However, these rules underspecify the relative order for many pairs of adjectives and are often difficult to apply in practice. In this paper, we will discuss a number of statistical and machine learning approaches to automatically extracting from large corpora the constraints on the order of prenominal adjectives in English. The problem of generating ordered sequences of adjectives is an instance of the more general problem of selecting among a number of possible outputs from a natural language generation system. One approach to this more general problem, taken by the 'Nitrogen' generator (Langkilde and Knight, 1998a; Langkilde and Knight, 1998b) , takes advantage of standard statistical techniques by generating a lattice of all possible strings given a semantic representation as input and selecting the most likely output using a bigram language model.
REF continues this work, determining the order for sequences of prenominal adjectives by examining several different statistical and machine learning techniques.
The Order Of Prenominal Adjectives In Natural Language Generation
Annual Meeting Of The Association For Computational Linguistics
Computer Science