Machine Translation dates back to the 1940s and rose in popularity in the mid-1990s with the advent of the World Wide Web. However, we know little about how people use machine translation in their lives. Daniel Liebling will present a brief history of machine translation, followed by some state of the art results. To what extent do algorithmic measurements correlate with end-user satisfaction? He will then cover recent ethnographic and systems research on how well machine translation currently serves its users, followed by opportunities for translation studies scholars to inform the development of future human + machine translation systems.
 

Daniel Liebling (he/him) is Staff Engineering Manager at Google Research where he leads a team of scientists and engineers on human language technologies like speech recognition and machine translation. Before Google, Dan worked on human-computer interaction and information retrieval at Microsoft Research. He has over 40 publications and 15 patents. Dan holds a M.S. in Computer Science and Engineering from UW and a B.S. in Engineering and Applied Science from Caltech.