This article describes in detail an n-gram approach to statistical machine translation. This
approach consists of a log-linear combination of a translation model based on n-grams of …

We present a novel machine translation model which models translation by a linear
sequence of operations. In contrast to the “N-gram” model, this sequence includes not only …

Word reordering is one of the most difficult aspects of statistical machine translation (SMT),
and an important factor of its quality and efficiency. Despite the vast amount of research …

The Internet gives us access to a wealth of information in languages we don't understand.
The investigation of automated or semi-automated approaches to translation has become a …

In this paper we describe an elegant and efficient approach to coupling reordering and
decoding in statistical machine translation, where the n-gram translation model is also …

Named entities are perhaps the most important indexing element in text for most of the
information extraction and mining tasks. Construction of a Named Entity Recognition (NER) …

In this paper, we develop an approach called syntax-based reordering (SBR) to handling the
fundamental problem of word ordering for statistical machine translation (SMT). We propose …

We describe two methods to improve SMT accuracy using shallow syntax information. First,
we use chunks to refine the set of word alignments typically used as a starting point in SMT …

This paper addresses the problem of word reordering in statistical machine translation. We
follow a word order monotonization strategy making use of syntax information (dependency …

An important problem in translation neglected by most recent statistical machine translation
systems is insertion and deletion of words, such as function words, motivated by linguistic …