Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation

To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation

Reordering poses a major challenge in machine translation (MT) between two languages with significant differences in word order. In this paper, we present a novel reordering approach utilizing sparse features based on de…

Effective Online Reordering with Arc-Eager Transitions

We present a new transition system with word reordering for unrestricted non-projective dependency parsing. Our system is based on decomposed arc-eager rather than arc-standard, which allows more flexible ambiguity resol…

A Dependency-Based Neural Reordering Model for Statistical Machine Translation

In machine translation (MT) that involves translating between two languages with significant differences in word order, determining the correct word order of translated words is a major challenge. The dependency parse tr…

LSTM Neural Reordering Feature for Statistical Machine Translation

Artificial neural networks are powerful models, which have been widely applied into many aspects of machine translation, such as language modeling and translation modeling. Though notable improvements have been made in t…

Dependency Tree Abstraction for Long-Distance Reordering in Statistical Machine Translation