Toward more linguistically-informed translation models – Adam Lopez (Johns Hopkins University)
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Modern translation systems model translation as simple substitution and permutation of word tokens, sometimes informed by syntax. Formally, these models are probabilistic relations on regular or context-free sets, a poor fit for many of the world’s languages. Computational linguists have developed more expressive mathematical models of language that exhibit high empirical coverage of annotated language data, correctly predict a variety of important linguistic phenomena in many languages, explicitly model semantics, and can be processed with efficient algorithms. I will discuss some ways in which such models can be used in machine translation, focusing particularly on combinatory categorial grammar (CCG).
All Participant Lectures will be held in Room S1, 4th Floor.