A Statistical Approach for Similarity Measurement Between Sentences for EBMT Abstract

Success of Example-Based Machine Translation depends heavily on how efficient the retrieval scheme is. The more similar is the retrieved sentence to the input one, the easier will be the adaptation of the retrieved translation to the current requirement. However, there is no suitable scheme for measuring similarity between sentences. This paper reports preliminary results of a similarity measurement scheme that is based on a linear model, whose coefficients are determined by multiple regression technique. The data for the analysis has been collected from a survey of a number of respondents. Three major aspects of similarity, namely pragmatic, syntactic and semantic have been considered. Each respondent has been asked to evaluate the similarity between different pairs of sentences that are carefully designed to reflect one of the above types of similarity. A statistical analysis of these evaluations reveals general human perception about sentential similarity, which will help in designing a suitable retrieval scheme. 1. Introduction. Example-Based Machine Translation (EBMT) [Nagao, 1984][Brown, 1996] has of late become popular to facilitate automatic and/or semi-automatic Machine Translation. EBMT is based on the idea performing translation by imitating past translation examples.. In this type of translation system, a large amount of translation examples between two languages (L1 and L2, say, respectively the source and the targe