We apply machine learning to the Linear Ordering Problem in order to learn sentence-
specific reordering models for machine translation. We demonstrate that even when these …

In this paper, we describe a sourceside reordering method based on syntactic chunks for
phrase-based statistical machine translation. First, we shallow parse the source language …

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 …

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 …

In this paper we describe a new approach to model long-range word reorderings in
statistical machine translation (SMT). Until now, most SMT approaches are only able to …

In the last years, deep learning algorithms have highly revolutionized several areas
including speech, image and natural language processing. The specific field of Machine …

We present a unified view of many translation algorithms that synthesizes work on deductive
parsing, semiring parsing, and efficient approximate search algorithms. This gives rise to …

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 …

Structured syntactic knowledge is important for phrase reordering. This paper proposes
using convolution tree kernel over source parse tree to model structured syntactic …