tailieunhanh - Báo cáo khoa học: "The adaptation of a machine-learned sentence realization system to French"

We describe the adaptation to French of a machine-learned sentence realization system called Amalgam that was originally developed to be as language independent as possible and was first implemented for German. We discuss the development of the French implementation with particular attention to the degree to which the original system could be reused, and we present the results of a human evaluation of the quality of sentence realization using the new French system. | The adaptation of a machine-learned sentence realization system to French Martine Smets Michael Gamon Simon Corston-Oliver and Eric Ringger Microsoft Research One Microsoft Way Redmond WA 98052 USA martines mgamon simonco ringger @ Abstract We describe the adaptation to French of a machine-learned sentence realization system called Amalgam that was originally developed to be as language independent as possible and was first implemented for German. We discuss the development of the French implementation with particular attention to the degree to which the original system could be reused and we present the results of a human evaluation of the quality of sentence realization using the new French system. Introduction Recently statistical and machine-learned approaches have been applied to the sentence realization phase of natural language generation. The Nitrogen system for example uses a word bigram language model to score and rank a large set of alternative sentence realizations Langkilde and Knight 1998a 1998b . Other recent approaches use syntactic representations. FERGUS Bangalore and Rambow 2000 Halogen Langkilde 2000 Langki Ide-Geary 2002 and Amalgam Corston-Oliver et al. 2002 use syntactic trees as an intermediate representation to determine the optimal string output. The Amalgam system discussed here is a sentence realization system which maps a semantic representation to a surface syntactic tree via intermediate syntactic representations. The mappings are performed with linguistic operations the context for which is primarily machine-learned. The resulting syntactic tree contains all the necessary information on its leaf nodes from which a surface string can be read. The promise of machine-learned approaches to sentence realization is that they can easily be adapted to new domains and ideally to new languages merely by retraining. The architecture of Amalgam was intended to be languageindependent although the system has previously only been .