Applying Explanation-based Learning to Control and Speeding-up Natural Language Generation

Applying Explanation-based Learning to Control and Speeding-up Natural Language Generation Giinter Neumann DFKI GmbH Stuhlsatzenhausweg 3 66123 Saarbriicken, Germany neumann@df k i. uni- sb. de Abstract This paper presents a method for the au- tomatic extraction of subgrammars to con- trol and speeding-up natural language gen- eration NLG. The method is based on explanation-based learning EBL. The main advantage for the proposed new method for NLG is that the complexity of the grammatical decision making process dur- ing NLG can be vastly reduced, because the EBL method supports the adaption of a NLG system to a particular use of a lan- guage. 1 Introduction In recent years, a Machine Learning tech- nique known as Explanation-based Learning EBL (Mitchell, Keller, and Kedar-Cabelli, 1986; van Harmelen and Bundy, 1988; Minton et al., 1989) has successfully been applied to control and speeding-up natural language parsing (Rayner, 1988; Samuelsson and Rayner, 1991; Neumann, 1994a; Samuelsson, 1994; Srinivas and Joshi, 1995; Rayner and Carter, 1996). The core idea of EBL is to transform the derivations (or explanations) computed by a prob- lem solver (e.g., a parser) to some generalized and compact forms, which can be used very efficiently for solving similar problems in the future. EBL has primarily been used for parsing to automatically spe- cialize a given source grammar to a specific domain. In that case, EBL is used as a method for adapting a general grammar and/or parser to the sub-language defined by a suitable training corpus (Rayner and Carter, 1996). A specialized grammar can be seen as describ- ing a domain-specific set of prototypical construc- tions. Therefore, the EBL approach is also very interesting for natural language generation (NLG). Informally, NLG is the production of a natural language text from computer-internal representa- tion of information, where NLG can be seen as a complex--potentially cascaded--decision making process. Commonly, a NLG system is decomposed into two major components, viz. the strategic com- ponent which decides 'what to say' and the tacti- cal component which decides 'how to say' the result of the strategic component. The input of the tacti- cal component is basically a semantic representation computed by the strategic component. Using a lexi- con and a grammar, its main task is the computation of potentially all possible strings associated with a semantic input. Now, in the same sense as EBL is used in parsing as a means to control the range of possible strings as well as their degree of ambigu- ity, it can also be used for the tactical component to control the range of possible semantic input and their degree of paraphrases. In this paper, we present a novel method for the automatic extraction of subgrammars for the control and speeding-up of natural language generation. Its main advantage for NLG is that the complexity of the (linguistically oriented) decision making process during natural language generation can be vastly re- duced, because the EBL method supports adaption of a NLG system to a particular language use. The core properties of this new …

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