LIUM's SMT Machine Translation Systems for WMT 2012

2010

In this paper we describe the statistical machine translation system of the RWTH Aachen University developed for the translation task of the Fifth Workshop on Statistical Machine Translation. Stateof-the-art phrase-based and hierarchical statistical MT systems are augmented with appropriate morpho-syntactic enhancements, as well as alternative phrase training methods and extended lexicon models. For some tasks, a system combination of the best systems was used to generate a final hypothesis. We participated in the constrained condition of German-English and French-English in each translation direction.

… on Statistical Machine Translation …, 2010

This paper describes the system submitted by the Laboratory of Informatics of Grenoble (LIG) for the fifth Workshop on Statistical Machine Translation. We participated to the news shared translation task for the French-English language pair. We investigated differents techniques to simply deal with Out-Of-Vocabulary words in a statistical phrase-based machine translation system and analyze their impact on translation quality. The final submission is a combination between a standard phrase-based system using the Moses decoder, with appropriate setups and pre-processing, and a lemmatized system to deal with Out-Of-Vocabulary conjugated verbs.

Proceedings of the Ninth Workshop on Statistical Machine Translation, 2014

We describe Stanford's participation in the French-English and English-German tracks of the 2014 Workshop on Statistical Machine Translation (WMT). Our systems used large feature sets, word classes, and an optional unconstrained language model. Among constrained systems, ours performed the best according to uncased BLEU: 36.0% for French-English and 20.9% for English-German.

This work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign International Workshop on Spoken Language Translation (IWSLT) 2013. We participated in the English→French, English↔German, Arabic→English, Chinese→English and Slovenian↔English MT tracks and the English→French and English→German SLT tracks. We apply phrase-based and hierarchical SMT decoders, which are augmented by state-of-the-art extensions. The novel techniques we experimentally evaluate include discriminative phrase training, a continuous space language model, a hierarchical reordering model, a word class language model, domain adaptation via data selection and system combination of standard and reverse order models. By application of these methods we can show considerable improvements over the respective baseline systems.

Proceedings of the Ninth Workshop on Statistical Machine Translation, 2014

This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the German→English translation task of the ACL 2014 Eighth Workshop on Statistical Machine Translation (WMT 2014). Both hierarchical and phrase-based SMT systems are applied employing hierarchical phrase reordering and word class language models. For the phrase-based system, we run discriminative phrase training. In addition, we describe our preprocessing pipeline for German→English.

Proceedings of the Sixth Workshop on Statistical Machine Translation}

The focus of our workshop was to use parallel corpora for machine translation. Recent experimentation has shown that the performance of SMT systems varies greatly with the source language. In this workshop we encouraged researchers to investigate ways to improve the performance of SMT systems for diverse languages, including morphologically more complex languages, languages with partial free word order, and low-resource languages.

Proceedings of the Fourth Workshop on Statistical Machine Translation - StatMT '09, 2009

This paper presents the results of the WMT09 shared tasks, which included a translation task, a system combination task, and an evaluation task. We conducted a large-scale manual evaluation of 87 machine translation systems and 22 system combination entries. We used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality, for more than 20 metrics. We present a new evaluation technique whereby system output is edited and judged for correctness. 2 Overview of the shared translation and system combination tasks The workshop examined translation between English and five other languages: German, Spanish, French, Czech, and Hungarian. We created a test set for each language pair by translating newspaper articles. We additionally provided training data and a baseline system. 2.1 Test data The test data for this year's task was created by hiring people to translate news articles that were drawn from a variety of sources during the period from the end of September to mid-October of 2008. A total of 136 articles were selected, in roughly equal amounts from a variety of Czech, English, French, German, Hungarian, Italian and Spanish news sites: 3

2009

This paper presents the results of the WMT09 shared tasks, which included a translation task, a system combination task, and an evaluation task. We conducted a large-scale manual evaluation of 87 machine translation systems and 22 system combination entries. We used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality, for more than 20 metrics. We present a new evaluation technique whereby system output is edited and judged for correctness. 2.1 Test data The test data for this year's task was created by hiring people to translate news articles that were drawn from a variety of sources during the period from the end of September to mid-October of 2008. A total of 136 articles were selected, in roughly equal amounts from a variety of Czech, English, French, German, Hungarian, Italian and Spanish news sites: 2

The 2005 International Workshop on Spoken Language Translation, 2005

In this paper we describe the CMU statistical machine translation system used in the IWSLT 2005 evaluation campaign. This system is based on phrase-to-phrase translations extracted from a bilingual corpus. We experimented with two different phrase extraction methods; PESA on-the-fly phrase extraction and alignment free extraction method. The translation model, language model and other features were combined in a log-linear model during decoding. We present our experiments on model adaptation for new data in ...

This paper describes the University of Washington’s submis-sion to the IWSLT 2006 evaluation campaign. We presenta multi-pass statistical phrase-basedmachine translation sys-tem for the Italian-English open-data track. The focus of ourwork was on the use of heterogeneous data sources for train-ing translation and language models, the use of several novelrescoring features in the second pass, and exploiting N-bestinformation for translation in the ASR-output condition. Re-sults show mixed benefits of adding out-of-domain data andusing N-best information and demonstrate improvements forsome of the novel rescoring features.