Integrating Phrase-based Reordering Features into a Chart-based Decoder for Machine Translation

Neural Reordering Model Considering Phrase Translation and Word Alignment for Phrase-based Translation

This paper presents an improved lexicalized reordering model for phrase-based statistical machine translation using a deep neural network. Lexicalized reordering suffers from reordering ambiguity, data sparseness and noi…

Phrase-based Machine Translation using Multiple Preordering Candidates

In this paper, we propose a new decoding method for phrase-based statistical machine translation which directly uses multiple preordering candidates as a graph structure. Compared with previous phrase-based decoding meth…

Which Words Matter in Defining Phrase Reordering Behavior in Statistical Machine Translation?

Lexicalized and hierarchical reordering models use relative frequencies of fully lexicalized phrase pairs to learn phrase reordering distributions. This results in unreliable estimation for infrequent phrase pairs which …

Improved Reordering for Phrase-Based Translation using Sparse Features

Dynamically Shaping the Reordering Search Space of Phrase-Based Statistical Machine Translation

Defining the reordering search space is a crucial issue in phrase-based SMT between distant languages. In fact, the optimal trade-off between accuracy and complexity of decoding is nowadays reached by harshly limiting th…