Dependency-Based Bilingual Language Models for Reordering in Statistical 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…

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…

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

Using unlabeled dependency parsing for pre-reordering for Chinese-to-Japanese statistical machine translation

Explicit Reordering for Neural Machine Translation

In Transformer-based neural machine translation (NMT), the positional encoding mechanism helps the self-attention networks to learn the source representation with order dependency, which makes the Transformer-based NMT a…