Articles

Statistical machine translation (SMT) per- formance suffers when models are trained on only small amounts of parallel data. The learned models typically have both low accuracy (incorrect translations and feature scores) and low coverage (high out-of-vocabulary rates). In this work, we use an additional data resource, compa- rable corpora, to …...

Many tasks in NLP and IR require ef- ficient document similarity computations. Beyond their common application to ex- ploratory data analysis, latent variable topic models have been used to represent text in a low-dimensional space, indepen- dent of vocabulary, where documents may be compared. This paper focuses on the task …...

We propose a technique for improving the quality of phrase-based translation systems by creating synthetic translation options—phrasal translations that are gen- erated by auxiliary translation and post- editing processes—to augment the de- fault phrase inventory learned from par- allel data. We apply our technique to the problem of producing English …...

Supervised approaches to NLP tasks rely on high-quality data annotations, which typically result from expensive manual la- belling procedures. For some tasks, how- ever, the subjectivity of human judgements might reduce the usefulness of the an- notation for real-world applications. In Machine Translation (MT) Quality Esti- mation (QE), for instance, …...

We present 5 systems of the Munich- Edinburgh-Stuttgart1 joint submissions to the 2013 SMT Shared Task: FR-EN, EN- FR, RU-EN, DE-EN and EN-DE. The first three systems employ inflectional gen- eralization, while the latter two employ parser-based reordering, and DE-EN per- forms compound splitting. For our ex- periments, we use …...

We describe the Uppsala University sys- tem for WMT13, for English-to-German translation. We use the Docent decoder, a local search decoder that translates at the document level. We add tunable dis- tortion limits, that is, soft constraints on the maximum distortion allowed, to Do- cent. We also investigate cleaning of …...

This paper describes QCRI-MES’s sub- mission on the English-Russian dataset to the Eighth Workshop on Statistical Ma- chine Translation. We generate improved word alignment of the training data by incorporating an unsupervised translitera- tion mining module to GIZA++ and build a phrase-based machine translation sys- tem. For tuning, we use …...

This paper presents the experiments con- ducted by the Machine Translation group at DCU and Prompsit Language Engineer- ing for the WMT13 translation task. Three language pairs are considered: Spanish- English and French-English in both direc- tions and German-English in that direc- tion. For the Spanish-English pair, the use of …...

We describe improvements made over the past year to Joshua, an open-source trans- lation system for parsing-based machine translation. The main contributions this past year are significant improvements in both speed and usability of the grammar extraction and decoding steps. We have also rewritten the decoder to use a sparse …...

This paper describes the University of Cambridge submission to the Eighth Workshop on Statistical Machine Transla- tion. We report results for the Russian- English translation task. We use mul- tiple segmentations for the Russian in- put language. We employ the Hadoop framework to extract rules. The decoder is HiFST, a …...

This paper describes the joint submis- sion of the QUAERO project for the German→English translation task of the ACL 2013 Eighth Workshop on Statisti- cal Machine Translation (WMT 2013). The submission was a system combina- tion of the output of four different transla- tion systems provided by RWTH Aachen University, …...

This paper describes the statistical ma- chine translation (SMT) systems devel- oped at RWTH Aachen University for the translation task of the ACL 2013 Eighth Workshop on Statistical Machine Translation (WMT 2013). We partici- pated in the evaluation campaign for the French-English and German-English lan- guage pairs in both translation …...

This paper describes shallow semantically-informed Hierarchical Phrase-based SMT (HPBSMT) and Phrase-Based SMT (PBSMT) systems developed at Dublin City University for participation in the translation task between EN-ES and ES-EN at the Work- shop on Statistical Machine Translation (WMT 13). The system uses PBSMT and HPBSMT decoders with multiple LMs, but …...

We propose a pre-reordering scheme to improve the quality of machine translation by permuting the words of a source sen- tence to a target-like order. This is accom- plished as a transition-based system that walks on the dependency parse tree of the sentence and emits words in target-like or- der, …...

This paper describes OmnifluentTM Trans- late – a state-of-the-art hybrid MT sys- tem capable of high-quality, high-speed translations of text and speech. The sys- tem participated in the English-to-French and Russian-to-English WMT evaluation tasks with competitive results. The features which contributed the most to high translation quality were training data …...

We describe the LIA machine transla- tion systems for the Russian-English and English-Russian translation tasks. Various factored translation systems were built us- ing MOSES to take into account the mor- phological complexity of Russian and we experimented with the romanization of un- translated Russian words. 1...

We present two English-to-Czech systems that took part in the WMT 2013 shared task: TECTOMT and PHRASEFIX. The former is a deep-syntactic transfer-based system, the latter is a more-or-less stan- dard statistical post-editing (SPE) applied on top of TECTOMT. In a brief survey, we put SPE in context with other …...

This paper describes the TALP participa- tion in the WMT13 evaluation campaign. Our participation is based on the combi- nation of several statistical machine trans- lation systems: based on standard phrase- based Moses systems. Variations include techniques such as morphology genera- tion, training sentence filtering, and do- main adaptation through …...

We present the system we developed to provide efficient large-scale feature-rich discriminative training for machine trans- lation. We describe how we integrate with MapReduce using Hadoop streaming to allow arbitrarily scaling the tuning set and utilizing a sparse feature set. We report our findings on German-English and Russian- English translation, …...

This paper describes Munich-Edinburgh- Stuttgart’s submissions to the Eighth Workshop on Statistical Machine Transla- tion. We report results of the translation tasks from German, Spanish, Czech and Russian into English and from English to German, Spanish, Czech, French and Rus- sian. The systems described in this paper use OSM (Operation …...