Sentence Planning as Description Using Tree Adjoining Grammar

Sentence Planning as Description Using Tree Adjoining Grammar * Matthew Stone Christine Doran Department of Computer and Information Science Department of Linguistics University of Pennsylvania University of Pennsylvania Philadelphia, PA 19014 Philadelphia, PA 19014 matthew@linc, cis. upenn, edu cdoran@linc, cis. upenn, edu Abstract We present an algorithm for simultaneously constructing both the syntax and semantics of a sentence using a Lexicalized Tree Adjoin- ing Grammar (LTAG). This approach captures naturally and elegantly the interaction between pragmatic and syntactic constraints on descrip- tions in a sentence, and the inferential interac- tions between multiple descriptions in a sen- tence. At the same time, it exploits linguis- tically motivated, declarative specifications of the discourse functions of syntactic construc- tions to make contextually appropriate syntac- tic choices. 1 Introduction Since (Meteer, 1991), researchers in natural language generation have recognized the need to refine and re- organize content after the rhetorical organization of ar- guments and before the syntactic realization of phrases. This process has been named sentence planning (Ram- bow and Korelsky, 1992). Broadly speaking, it involves aggregating content into sentence-sized units, and then selecting the lexical and syntactic elements that are used in realizing each sentence. Here, we consider this second process. The challenge lies in integrating constraints from syn- tax, semantics and pragmatics. Although most generation systems pipeline decisions (Reiter, 1994), we believe the most efficient and flexible way to integrate constraints in sentence planning is to synchronize the decisions. In this paper, we provide a natural framework for dealing with interactions and ensuring contextually appropriate output in a single pass. As in (Yang et al., 1991), Lex- icalized Tree Adjoining Grammar (LTAG) provides an *The authors thank Aravind Joshi, Mark Steedman, Martha Palmer, Ellen Prince, Owen Rambow, Mike White, Betty Birner, and the participants of INLG96 for their helpful com- ments on various incarnations of this work. This work has been supported by NSF and IRCS graduate fellowships, NSF grant NSF-STC SBR 8920230, ARPA grant N00014-94 and ARt grant DAAH04-94-G0426. abstraction of the combinatorial properties of words. We combine LTAG syntax with declarative specifications of semantics and pragmatics of words and constructions, so that we can build the syntax and semantics of sentences simultaneously. To drive this process, we take descrip- tion as the paradigm for sentence planning. Our planner, SPUD (Sentence Planner Using Descriptions), takes in a collection of goals to achieve in describing an event or state in the world; SPUD incrementally and recursively applies lexical specifications to determine which entities to describe and what information to include about them. Our system is unique in the streamlined organization of the grammar, and in its evaluation both of contextual ap- propriateness of pragmatics and of descriptive adequacy of semantics. The organization of the paper is as follows. In sec- tion 2, we review research on generating referring ex- pressions and motivate our treatment of sentences as re- ferring expressions. Then, in section 3, we present the linguistic underpinnings of our work. In section 4, we describe our algorithm and its …

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