Best-First Surface Realization

Stephan Busemann
DFKI GmbH
Stuhlsatzenhausweg 3
D-66123 Saarbr?ucken
email: [email protected]

Abstract

Current work in surface realization concentrates on the use of general, abstract algorithms that interpret large, reversible grammars. Only little attention has been paid so far to the many small and simple applications that require coverage of a small sublanguage at different degrees of sophistication. The system TG/2 described in this paper1 can be smoothly integrated with deep generation processes, it integrates canned text, templates, and context-free rules into a single formalism, it allows for both textual and tabular output, and it can be parameterized according to linguistic preferences. These features are based on suitably restricted production system techniques and on a generic backtracking regime.

1 Motivation

Current work in surface realization concentrates on the use of general, abstract algorithms that interpret declaratively defined, non-directional grammars. It is claimed that this way, a grammar can be reused for parsing and generation, or a generator can interpret different grammars (e.g. in machine translation). A prominent example for this type of abstract algorithm is semantic-head-driven generation [Shieber et al., 1990] that has been used with HPSG, CUG, DCG and several other formalisms.

In practice, this type of surface realization has several drawbacks. First, many existing grammars have been developed with parsing as the primary type of processing in mind. Adapting their semantics layer to a generation algorithm, and thus achieving reversibility, can turn out to be a difficult enterprise [Russell et al., 1990]. Second, many linguistically motivated grammars do not cover common means of information presentation, such as filling in a table, bulletized lists, or semi-frozen formulae used for greetings in letters. Finally, the grammar-based logical form representation hardly serves as a suitable interface to deep generation processes. Grammar-based semantics is, to a large extent, a compositional reflex of the syntactic structure and hence corresponds too closely to the surface form to be generated. As a consequence, only little attention has been paid to interfacing this type of realizers adequately to deep generation processes, e.g. by allowing the latter to influence the order of results of the former.
1This paper appears in Proc. 8th International Workshop on Natural Language Generation, Herstmonceux, Sussex, Great Britain, June 1996. I am grateful to Michael Wein, who implemented the interpreter, and to Jan Alexandersson for influential work on a previous version of the system. Finally, I wish to thank two anonymous reviewers for useful suggestions. All errors contained in this paper are my own.