Sentence transduction: The most intensively studied type of statistical sentence transduction to date has been statistical MT (SMT) (e.g., Koehn 2010; May 2012). Its successes beginning in the late 1980s and early 90s came as something of a surprise to the NLP community, which had been rather pessimistic about MT prospects ever since the report by Bar-Hillel (1960) and the ALPAC report (Pierce et al. 1966), negatively assessing the results of a major post-WW2 funding push in MT by the US government. MT came to be viewed as a large-scale engineering enterprise that would not have broad impacts until it could be adequately integrated with semantics and knowledge-based inference. The statistical approach emerged in the wake of successful application of “noisy channel” models to speech recognition in the late 1970s and during the 80s, and was propelled forward by new developments in machine learning and the increasing availability of large machine-readable linguistic corpora, including parallel texts in multiple languages.