Researchers from the IBM T.J. Watson Research Center publish "A Statistical Approach to Language Translation," marking a pivotal shift from rule-based to probabilistic methods in machine translation.23 This approach, exemplified by IBM's Candide project24, uses 2.2 million English-French sentence pairs, primarily sourced from the Canadian Parliament's proceedings. This new methodology emphasizes learning from statistical patterns in data rather than attempting to comprehend or "understand" the languages, reflecting the broader trend toward machine learning that relies on analyzing known examples. This probabilistic model paved the way for many future advancements in natural language processing and machine translation.
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