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In this article, we present a novel machine translation model, the Operation Sequence Model (OSM), which combines the benefits of phrase-based and N-gram-based statistical machine translation (SMT) an...
Recent research has shown clear improvement in translation quality by exploiting linguistic syntax for either the source or target language. However, when using syntax for both languages (“tree-to-t...
In this article we describe HiFST, a lattice-based decoder for hierarchical phrase-based translation and alignment. The decoder is implemented with standard Weighted Finite-State Transducer (WFST) ope...
Work on prepositional phrase (PP) attachment resolution generally assumes that there is an oracle that provides the two hypothesized structures that we want to choose between. The information that the...
We present a statistical machine translation model that uses hierarchical phrases—phrases that contain subphrases. The model is formally a synchronous context-free grammar but is learned from a para...
In this article we refine the formulation of the problem of prepositional phrase (PP) attachment as a four-way disambiguation problem. We argue that, in interpreting PPs, both knowledge about the site...
Current research in automatic single-document summarization is dominated by two effective,yet naıve approaches: summarization by sentence extraction and headline generation via bagof-words models...
While phrase-based statistical machine translation systems currently deliver state-of-theart performance, they remain weak on word order changes. Current phrase reordering models can properly handle s...
The alignment problem—establishing links between corresponding phrases in two related sentences—is as important in natural language inference (NLI) as it is in machine translation (MT). But the tools ...
The hierarchical phrase-based (HPB) transla-tion exploits the power of grammar to perform long distance reorderings, without specifying nonterminal orientations against adjacent blocks or considering ...

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