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Word-aligned bilingual corpora are an important knowledge source for many tasks in natural language processing. We improve the well-known IBM alignment models, as well as the Hidden-Markov alignment model using …...

In this paper, we address the word alignment problem for statistical machine translation. We aim at creating a sym- metric word alignment allowing for reli- able one-to-many and many-to-one word …...

AbstractState-of-the-art statistical machine translation is based on alignments between phrases – sequences of words in the source and target sentences. The learning step in these systems often relies on alignments …...

We introduce alignment models for Machine Trans- lation that take into account the context of a source word when determining its translation. Since the use of these contexts alone causes …...

We present a discriminative model that di- rectly predicts which set of phrasal transla- tion rules should be extracted from a sen- tence pair. Our model scores extraction sets: nested …...

Syntactic machine translation systems cur- rently use word alignments to infer syntactic correspondences between the source and tar- get languages. Instead, we propose an un- supervised ITG alignment model that …...

Current neural translation networks are based on an effective attention mechanism that can be considered as an implicit probabilistic notion of alignment. Such architectures do not guarantee a high quality …...

AbstractWe tackle the problem of monolingual phrase alignment conforming to syntactic structures. The existing method formalises the problem as unordered tree mapping; hence, the alignment quality is easily affected by …...