2,814 research outputs found
TMX markup: a challenge when adapting SMT to the localisation environment
Translation memory (TM) plays an important role in localisation workflows and is used as an efficient and fundamental tool to carry out translation. In recent years, statistical machine translation (SMT) techniques have been rapidly developed, and the translation quality and speed have been significantly improved as well. However,when applying SMT technique to facilitate post-editing in the localisation industry, we need to adapt SMT to the TM data which is formatted with special mark-up. In this paper, we explore some issues when adapting SMT to Symantec formatted TM data.
Three different methods are proposed to handle the Translation Memory eXchange (TMX) markup and a comparative study is carried out between them. Furthermore, we also compare the TMX-based SMT systems with a customised SYSTRAN system through human evaluation and automatic evaluation metrics. The experimental results conducted on the French and English language pair show that the SMT can perform well using TMX as input format either during training or at runtime
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
An Enhancement Method for Japanese-English Automated Translation
We present a method for improving existing statistical machine translation methods using a knowledge base compiled from a bilingual corpus as well as sequence alignment and pattern matching techniques from the area of machine learning and bioinformatics. An alignment algorithm identifies similar sentences, which are then used to construct a better word order for the translation. Our preliminary test results indicate a significant improvement of the translation quality.
TectoMT â a deep-Âlinguistic core of the combined Chimera MT system
Chimera is a machine translation system that combines the TectoMT deep-linguistic core with phrase-based MT system Moses. For EnglishâCzech pair it also uses the Depfix post-correction system. All the components run on Unix/Linux platform and are open source (available from Perl repository CPAN and the LINDAT/CLARIN repository). The main website is https://ufal.mff.cuni.cz/tectomt. The development is currently supported by the QTLeap 7th FP project (http://qtleap.eu)
Applying digital content management to support localisation
The retrieval and presentation of digital content such as that on the World Wide Web (WWW) is a substantial area of research. While recent years have seen huge expansion in the size of web-based archives that can be searched efficiently by commercial search engines, the presentation of potentially relevant content is still limited to ranked document lists represented by simple text snippets or image keyframe surrogates. There is expanding interest in techniques to personalise the presentation of content to improve the richness and effectiveness of the user experience. One of the most significant challenges to achieving this is the increasingly multilingual nature of this data, and the need to provide suitably localised responses to users based on this content. The Digital Content Management (DCM) track of the Centre for Next Generation Localisation (CNGL) is seeking to develop technologies to support advanced personalised access and presentation of information by combining elements from the existing research areas of Adaptive Hypermedia and Information Retrieval. The combination of these technologies is intended to produce significant improvements in the way users access information. We review key features of these technologies and introduce early ideas for how these technologies can support localisation and localised content before concluding with some impressions of future directions in DCM
MMT: New Open Source MT for the Translation Industry
MMT is a new open source machine translation
software specifically addressing the
needs of the translation industry. In this
paper we describe its overall architecture
and provide details about its major components.
We report performance results
on a multi-domain benchmark based on
public data, on two translation directions,
by comparing MMT against state-of-theart
commercial and research phrase-based
and neural MT systems
A Hybrid Machine Translation Framework for an Improved Translation Workflow
Over the past few decades, due to a continuing surge in the amount of content being translated and ever increasing pressure to deliver high quality and high throughput translation, translation industries are focusing their interest on adopting advanced technologies such as machine translation (MT), and automatic post-editing (APE) in their translation workflows. Despite the progress of the technology, the roles of humans and machines essentially remain intact as MT/APE are moving from the peripheries of the translation field closer towards collaborative human-machine based MT/APE in modern translation workflows. Professional translators increasingly become post-editors correcting raw MT/APE output instead of translating from scratch which in turn increases productivity in terms of translation speed. The last decade has seen substantial growth in research and development activities on improving MT; usually concentrating on selected aspects of workflows starting from training data pre-processing techniques to core MT processes to post-editing methods. To date, however, complete MT workflows are less investigated than the core MT processes. In the research presented in this thesis, we investigate avenues towards achieving improved MT workflows. We study how different MT paradigms can be utilized and integrated to best effect. We also investigate how different upstream and downstream component technologies can be hybridized to achieve overall improved MT. Finally we include an investigation into human-machine collaborative MT by taking humans in the loop. In many of (but not all) the experiments presented in this thesis we focus on data scenarios provided by low resource language settings.Aufgrund des stetig ansteigenden Ăbersetzungsvolumens in den letzten Jahrzehnten und
gleichzeitig wachsendem Druck hohe QualitĂ€t innerhalb von kĂŒrzester Zeit liefern zu
mĂŒssen sind Ăbersetzungsdienstleister darauf angewiesen, moderne Technologien wie
Maschinelle Ăbersetzung (MT) und automatisches Post-Editing (APE) in den Ăbersetzungsworkflow
einzubinden. Trotz erheblicher Fortschritte dieser Technologien haben
sich die Rollen von Mensch und Maschine kaum verÀndert. MT/APE ist jedoch nunmehr
nicht mehr nur eine Randerscheinung, sondern wird im modernen Ăbersetzungsworkflow
zunehmend in Zusammenarbeit von Mensch und Maschine eingesetzt. FachĂŒbersetzer
werden immer mehr zu Post-Editoren und korrigieren den MT/APE-Output, statt wie
bisher Ăbersetzungen komplett neu anzufertigen. So kann die ProduktivitĂ€t bezĂŒglich
der Ăbersetzungsgeschwindigkeit gesteigert werden. Im letzten Jahrzehnt hat sich in den
Bereichen Forschung und Entwicklung zur Verbesserung von MT sehr viel getan: Einbindung
des vollstĂ€ndigen Ăbersetzungsworkflows von der Vorbereitung der Trainingsdaten
ĂŒber den eigentlichen MT-Prozess bis hin zu Post-Editing-Methoden. Der vollstĂ€ndige
Ăbersetzungsworkflow wird jedoch aus Datenperspektive weit weniger berĂŒcksichtigt
als der eigentliche MT-Prozess. In dieser Dissertation werden Wege hin zum
idealen oder zumindest verbesserten MT-Workflow untersucht. In den Experimenten
wird dabei besondere Aufmertsamfit auf die speziellen Belange von sprachen mit geringen
ressourcen gelegt. Es wird untersucht wie unterschiedliche MT-Paradigmen verwendet
und optimal integriert werden können. Des Weiteren wird dargestellt wie unterschiedliche
vor- und nachgelagerte Technologiekomponenten angepasst werden können, um insgesamt
einen besseren MT-Output zu generieren. AbschlieĂend wird gezeigt wie der Mensch in
den MT-Workflow intergriert werden kann. Das Ziel dieser Arbeit ist es verschiedene
Technologiekomponenten in den MT-Workflow zu integrieren um so einen verbesserten
Gesamtworkflow zu schaffen. HierfĂŒr werden hauptsĂ€chlich HybridisierungsansĂ€tze verwendet.
In dieser Arbeit werden auĂerdem Möglichkeiten untersucht, Menschen effektiv
als Post-Editoren einzubinden
Translation Alignment and Extraction Within a Lexica-Centered Iterative Workflow
This thesis addresses two closely related problems. The first, translation alignment, consists of identifying bilingual document pairs that are translations of each other within
multilingual document collections (document alignment); identifying sentences, titles,
etc, that are translations of each other within bilingual document pairs (sentence alignment); and identifying corresponding word and phrase translations within bilingual
sentence pairs (phrase alignment). The second is extraction of bilingual pairs of equivalent word and multi-word expressions, which we call translation equivalents (TEs), from sentence- and phrase-aligned parallel corpora.
While these same problems have been investigated by other authors, their focus has
been on fully unsupervised methods based mostly or exclusively on parallel corpora.
Bilingual lexica, which are basically lists of TEs, have not been considered or given enough importance as resources in the treatment of these problems. Human validation of TEs, which consists of manually classifying TEs as correct or incorrect translations, has also not been considered in the context of alignment and extraction. Validation strengthens the importance of infrequent TEs (most of the entries of a validated lexicon) that otherwise would be statistically unimportant.
The main goal of this thesis is to revisit the alignment and extraction problems in the
context of a lexica-centered iterative workflow that includes human validation. Therefore, the methods proposed in this thesis were designed to take advantage of knowledge accumulated in human-validated bilingual lexica and translation tables obtained by unsupervised methods. Phrase-level alignment is a stepping stone for several applications, including the extraction of new TEs, the creation of statistical machine translation systems, and the creation of bilingual concordances. Therefore, for phrase-level alignment, the higher accuracy of human-validated bilingual lexica is crucial for achieving higher quality results in these downstream applications.
There are two main conceptual contributions. The first is the coverage maximization
approach to alignment, which makes direct use of the information contained in a lexicon, or in translation tables when this is small or does not exist. The second is the introduction of translation patterns which combine novel and old ideas and enables precise and productive extraction of TEs. As material contributions, the alignment and extraction methods proposed in this thesis have produced source materials for three lines of research, in the context of three PhD theses (two of them already defended), all sharing with me the supervision of my advisor. The topics of these lines of research are statistical machine translation, algorithms and data structures for indexing and querying phrase-aligned parallel corpora, and bilingual lexica classification and generation. Four publications have resulted directly from the work presented in this thesis and twelve from the collaborative lines of research
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