The Candide System for Machine Translation

The Candide System for Machine Translation Adam L. Berger, Peter F. Brown,* Stephen A. Della Pietra, Vincent J. Della Pietra, John R. GiUett, John D. Lafferty, Robert L. Mercer,* Harry Printz, Luboi Urei IBM Thomas J. Watson Research Center P.O. Box 704 Yorktown Heights, NY 10598 ABSTRACT We present an overview of Candide, a system for automatic translation of French text to English text. Candide uses methods of information theory and statistics to develop a probability model of the translation process. This model, which is made to accord as closely as possible with a large body of French and English sentence pairs, is then used to generate English translations of previously unseen French sentences. This paper provides a tutorial in these methods, discussions of the training and operation of the system, and a summary of test results. 1. Introduction Candide is an experimental computer program, now in its fifth year of development at IBM, for translation of French text to Enghsh text. Our goal is to perform fuRy-automatic, high-quality text-to-text translation. However, because we are still far from achieving this goal, the program can be used in both fully-automatic and translator's-assistant modes. Our approach is founded upon the statistical analysis of lan- guage. Our chief tools axe the source-channel model of com- munication, parametric probability models of language and translation, and an assortment of numerical algorithms for training such models from examples. This paper presents el- ementary expositions of each of these ideas, and explains how they have been assembled to produce Caadide. In Section 2 we introduce the necessary ideas from informa- tion theory and statistics. The reader is assumed to know el- ementary probability theory at the level of [1]. In Sections 3 and 4 we discuss our language and translation models. In Section 5 we describe the operation of Candide as it trans- lates a French document. In Section 6 we present results of our internal evaluations and the AB.PA Machine Translation Project evaluations. Section 7 is a summary and conclusion. 2. Statistical Translation Consider the problem of translating French text to English text. Given a French sentence f, we imagine that it was originally rendered as an equivalent Enghsh sentence e. To obtain the French, the Enghsh was transmitted over a noisy communication channel, which has the curious property that English sentences sent into it emerge as their French trans- lations. The central assumption of Candide's design is that the characteristics of this channel can be determined experi- mentally, and expressed mathematically. *Current address: Renaissance Technologies, Stony Brook, NY ~ English-to-French I f e Channel " _[ French-to-English -] Decoder 6 Figure 1: The Source-Channel Formalism of Translation. Here f is the French text to be translated, e is the putative original English rendering, and 6 is the English translation. This formalism can be exploited to yield French-to-English translations as follows. Let us write Pr(e I f) for the probabil- ity that e was the original English rendering of the French f. Given a French sentence …

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