Bilingual word alignment forms the foun-dation of most approaches to statistical machine translation. Current word align-ment methods are predominantly based on generative models. In this paper, we demonstrate a discriminative approach to training simple word alignment mod-els that are comparable in accuracy to the more complex generative models nor-mally used. These models have the the advantages that they are easy to add fea-tures to and they allow fast optimization of model parameters using small amounts of annotated data.
Automatic word alignment is a key step in training statistical machine translation systems. Despite ...
In extant phrase-based statistical machine translation (SMT) systems, the transla-tion model relies ...
Word alignment is the problem of annotating parallel text with translational correspondence. Previou...
Bilingual word alignment forms the foundation of current work on statistical machine translation. ...
In most statistical machine translation systems, bilingual segments are extracted via word alignment...
We introduce a syntactically enhanced word alignment model that is more flex-ible than state-of-the-...
We introduce a syntactically enhanced word alignment model that is more flexible than state-of-the-a...
UnrestrictedAll state of the art statistical machine translation systems and many example-based mach...
In this paper, we propose an algorithm for aligning words with their translation in a bilingual corp...
We present a general framework to incor-porate prior knowledge such as heuristics or linguistic feat...
We introduce a discriminatively trained, globally normalized, log-linear variant of the lexical tran...
In this paper, we address the word alignment problem for statistical machine translation. We aim at ...
In most statistical machine translation (SMT) systems, bilingual segments are extracted via word ali...
In most statistical machine translation (SMT) systems, bilingual segments are ex-tracted via word al...
Word alignment is a key task for every innovative statistical machine translation (SMT) system. An a...
Automatic word alignment is a key step in training statistical machine translation systems. Despite ...
In extant phrase-based statistical machine translation (SMT) systems, the transla-tion model relies ...
Word alignment is the problem of annotating parallel text with translational correspondence. Previou...
Bilingual word alignment forms the foundation of current work on statistical machine translation. ...
In most statistical machine translation systems, bilingual segments are extracted via word alignment...
We introduce a syntactically enhanced word alignment model that is more flex-ible than state-of-the-...
We introduce a syntactically enhanced word alignment model that is more flexible than state-of-the-a...
UnrestrictedAll state of the art statistical machine translation systems and many example-based mach...
In this paper, we propose an algorithm for aligning words with their translation in a bilingual corp...
We present a general framework to incor-porate prior knowledge such as heuristics or linguistic feat...
We introduce a discriminatively trained, globally normalized, log-linear variant of the lexical tran...
In this paper, we address the word alignment problem for statistical machine translation. We aim at ...
In most statistical machine translation (SMT) systems, bilingual segments are extracted via word ali...
In most statistical machine translation (SMT) systems, bilingual segments are ex-tracted via word al...
Word alignment is a key task for every innovative statistical machine translation (SMT) system. An a...
Automatic word alignment is a key step in training statistical machine translation systems. Despite ...
In extant phrase-based statistical machine translation (SMT) systems, the transla-tion model relies ...
Word alignment is the problem of annotating parallel text with translational correspondence. Previou...