Probabilistic Inference for Machine Translation

By Phil Blunsom, Miles Osborne. 2008

52 citations recorded by the source corpus.

We advance the state-of-the-art for discriminatively trained machine translation systems by presenting novel probabilistic inference and search methods for synchronous grammars. By approximating the intractable space of all candidate translations produced by intersecting an ngram language model with a synchronous grammar, we are able to train and decode models incorporating millions of sparse, heterogeneous features. Further, we demonstrate the power of the discriminative training paradigm by extracting structured syntactic features, and achieving increases in translation performance.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Edinburgh’s Phrase-based Machine Translation Systems for WMT-14 (2014, background): “, 2013a) with 4 count-based supportive features, sparse domain indicator, phrase length, and count bin features (Blunsom and Osborne, 2008; Chiang et al., 2009), a distortion limit of 6, maximum phrase-length of 5, 100-best translation options, Minimum Bayes Risk decoding (Kumar and Byrne, 2004), Cu”
On the Impact of Various Types of Noise on Neural Machine Translation (2018, neutral): “…a lexically-driven 5-gram operation sequence model (OSM) (Durrani et al., 2013), sparse domain indicator, phrase length, and count bin features (Blunsom and Osborne, 2008; Chiang et al., 2009), a maximum phrase-length of 5, compact phrase table (Junczys-Dowmunt, 2012) minimum Bayes risk…”
Explaining and Generalizing Back-Translation through Wake-Sleep (2018, methodology): “…these previous papers in that we are interested in a principled interpretation of back-translation as a strategy, independent of the particular parameterization in place; our analysis will hold parameterization of a probabilistic MT model, e.g., Liang et al. (2006) and Blunsom and Osborne (2008).”
The JHU Machine Translation Systems for WMT 2016 (2016, background): “, 2013) with 4 count-based supportive features, sparse domain indicator, phrase length, and count bin features (Blunsom and Osborne, 2008; Chiang et al., 2009), a distortion limit of 6, maximum phrase-length of 5, 100-best translation options, compact phrase table (JunczysDowmunt, 2012) minimum Baye”