We describe the system developed by the team of the National University of Singapore for English to Spanish machine translation of News Commentary text for the WMT09 Shared Translation Task. Our approach is based on domain adaptation, combining a small in-domain News Commentary bi-text and a large out-of-domain one from the Europarl corpus, from which we built and combined two separate phrase t...

This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonym...

This paper describes SYSTRAN’s systems submitted to the WMT 2017 shared news translation task for English-German, in both translation directions. Our systems are built using OpenNMT1, an opensource neural machine translation system, implementing sequence-to-sequence models with LSTM encoder/decoders and attention. We experimented using monolingual data automatically back-translated. Our resulti...

This study presents the TALP-UPC submission to the EACL Fourth Worskhop on Statistical Machine Translation 2009 evaluation campaign. It outlines the architecture and configuration of the 2009 phrase-based statistical machine translation (SMT) system, putting emphasis on the major novelty of this year: combination of SMT systems implementing different word reordering algorithms. Traditionally, w...

This paper describes the University of Edinburgh’s phrase-based and syntax-based submissions to the shared translation tasks of the ACL 2016 First Conference on Machine Translation (WMT16). We submitted five phrase-based and five syntaxbased systems for the news task, plus one phrase-based system for the biomedical task.

Our participation in the shared translation task at WMT-08 focusses on news translation from English to French. Our main goal is to contrast a baseline version of the phrase-based MATRAX system, with a version that incorporates syntactic “coupling” features in order to discriminate translations produced by the baseline system. We report results comparing different feature combinations.

We present a new translation model that include undecorated hierarchical-style phrase rules, decorated source-syntax rules, and partially decorated rules. Results show an increase in translation performance of up to 0.8% BLEU for German–English translation when trained on the news-commentary corpus, using syntactic annotation from a source language parser. We also experimented with annotation f...

This paper describes the neural and phrase-based machine translation systems submitted by CUNI to English-Czech News Translation Task of WMT17. We experiment with synthetic data for training and try several system combination techniques, both neural and phrase-based. Our primary submission CU-CHIMERA ends up being phrase-based backbone which incorporates neural and deep-syntactic candidate tran...

We report the results of our work on automating the transliteration decision of named entities for English to Arabic machine translation. We construct a classification-based framework to automate this decision, evaluate our classifier both in the limited news and the diverse Wikipedia domains, and achieve promising accuracy. Moreover, we demonstrate a reduction of translation error and an impro...