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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 at Lane Schwartz; Chris Callison-burch In: http://www-csli.stanford.edu/~ccb/publications/hiero-grammar-extraction-with-suffix-arrays.pdf (2010) BASE Show details 2 Bilingual Lexicon Induction for Low-resource Languages Alexandre Klementiev; Chris Callison-Burch; Ann Irvine In: http://hltcoe.files.wordpress.com/2011/09/tr5bilinguallexicalinduction.pdf (2010) BASE Show details 3 Moving Beyond Phrase Pairs: The Relevance of the Corpus in a SMT World Aaron B. Phillips; Stephan Vogel; Noah A. Smith; Chris Callison-burch In: http://www.cs.cmu.edu/%7Eaphillip/publications/proposal.pdf (2010) Abstract: Machine translation has advanced considerably in recent years, but primarily due to the availability of larger data sets. Translation of low-frequency phrases and resourcepoor languages is still a serious problem. In this work we explore a deeper integration of context, structure, and similarity within machine translation. Instead of modeling phrase pairs in abstract, we propose modeling each instance of a translation in the corpus. Unlike the traditional SMT approach that builds a mixture of independent, simple distributions for each phrase pair, our model is a mixture of translation instances. The significance lies in that we use a distance measure to assesses the relevance of each translation instance. It permits simple the integration of instance-specific features which we plan to exploit in three key directions. First, we will introduce non-local features that identify the relevant context of an instance in order to favor those that are most similar to the input. Second, we will mark-up the corpus with metadata from multiple external sources to sharpen the scoring of each translation instance and guide the overall translation process. Third, we will identify URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.188.2673 http://www.cs.cmu.edu/%7Eaphillip/publications/proposal.pdf BASE Hide details 4 Visualizing data structures in parsing-based machine translation. The Prague Bulletin of Mathematical Linguistics Jonathan Weese; Chris Callison-burch In: http://ufal.mff.cuni.cz/pbml/93/art-weese-callison-burch.pdf (2010) BASE Show details

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