2013 11 07 Chris Dyer - Translation into Morphologically Rich Languages with a Hierarchical Model
Channel: Yoav Artzi
Date: 2014-03-08

Abstract:
Morphologically rich languages challenge the assumptions made in statistical models of translation. On one hand, the independence assumptions don't go far enough: opportunities to share statistical strength across related lexical items are missed (as a result, excessive amounts of data are required to reliably estimate model parameters). On the other hand, the independence assumptions go too far: decomposing translations into independent events fails to capture large-scale grammatical structure. While previous attempts to remedy this situation have been numerous, they tend to be highly language-dependent, or they failed from a modeling perspective: they improve performance on morphologically regular long-tail types at the expense of frequent --- but often idiosyncratic --- word types.

We present a solution to these problems that conceives of translation in terms of a hierarchical model. First, a sentence-specific translation model (i.e., a set of stochastic translation rules) is generated by predicting inflection paradigms of target words, given their source context. Second, this translation model is applied to translate the source sentence. Our approach can be understood as smoothing a surface-form translation model by backing off to a model of the translation process that applies at the morpheme level. We report significant improvements in translation quality when translating from English into Russian, Hebrew and Swahili. Finally, our approach relies on morphological analysis of the target language but we show that an unsupervised Bayesian model can also be used in place of a standard supervised analyzer.

Joint work with Victor Chahuneau, Eva Schlinger, and Yulia Tsvetkov.

Bio:
Chris Dyer is an assistant professor in the Language Technologies Institute and Machine Learning Departments at Carnegie Mellon University. He completed his PhD on statistical machine translation with Philip Resnik at the University of Maryland in 2010, and was then a postdoc with Noah Smith. Together with Jimmy Lin, he is author of "Data-Intensive Text Processing with MapReduce", published by Morgan & Claypool in 2010. Current research interests include machine translation, unsupervised learning, Bayesian techniques, lexical semantics, and representation learning.