Design of a hybrid high quality machine translation system

Authors:
Kurt Eberle;Johanna Geiß;Mireia Ginestí-Rosell;Bogdan Babych;Anthony Hartley;Reinhard Rapp;Serge Sharoff;Martin Thomas
Affiliations:
Lingenio GmbH, Karlsruher Straße, Heidelberg, Germany;Lingenio GmbH, Karlsruher Straße, Heidelberg, Germany;Lingenio GmbH, Karlsruher Straße, Heidelberg, Germany;University of Leeds, Leeds, UK;University of Leeds, Leeds, UK;University of Leeds, Leeds, UK;University of Leeds, Leeds, UK;University of Leeds, Leeds, UK
Venue:
EACL 2012 Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra)
Year:
2012

Citing 18
Cited 0

Handling Translation Divergences: Combining Statistical and Symbolic Techniques in Generation-Heavy Machine Translation

AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
A systematic comparison of various statistical alignment models

Computational Linguistics
A program for aligning sentences in bilingual corpora

Computational Linguistics - Special issue on using large corpora: I
Retrieving collocations from text: Xtract

Computational Linguistics - Special issue on using large corpora: I
Bitext maps and alignment via pattern recognition

Computational Linguistics
Three heads are better than one

ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Identifying word translations in non-parallel texts

ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Toward memory-based translation

COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Automatic identification of word translations from unrelated English and German corpora

ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Mining the Web for bilingual text

ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Discriminative training and maximum entropy models for statistical machine translation

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Improving Machine Translation Performance by Exploiting Non-Parallel Corpora

Computational Linguistics
Learning a translation lexicon from monolingual corpora

ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
Hybrid data-driven models of machine translation

Machine Translation
Unsupervised Multilingual Sentence Boundary Detection

Computational Linguistics
Using comparable corpora to solve problems difficult for human translators

COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Inversion transduction grammar constraints for mining parallel sentences from quasi-comparable corpora

IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper gives an overview of the ongoing FP7 project HyghTra (2010--2014). The HyghTra project is conducted in a partnership between academia and industry involving the University of Leeds and Lingenio GmbH (company). It adopts a hybrid and bootstrapping approach to the enhancement of MT quality by applying rule-based analysis and statistical evaluation techniques to both parallel and comparable corpora in order to extract linguistic information and enrich the lexical and syntactic resources of the underlying (rule-based) MT system that is used for analysing the corpora. The project places special emphasis on the extension of systems to new language pairs and corresponding rapid, automated creation of high quality resources. The techniques are fielded and evaluated within an existing commercial MT environment.