Ventsislav Zhechev


2014

bib
Term translation central: up-to-date MT without frequent retraining
Ventsislav Zhechev
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Users Track

2012

pdf bib
Machine Translation Infrastructure and Post-editing Performance at Autodesk
Ventsislav Zhechev
Workshop on Post-Editing Technology and Practice

In this paper, we present the Moses-based infrastructure we developed and use as a productivity tool for the localisation of software documentation and user interface (UI) strings at Autodesk into twelve languages. We describe the adjustments we have made to the machine translation (MT) training workflow to suit our needs and environment, our server environment and the MT Info Service that handles all translation requests and allows the integration of MT in our various localisation systems. We also present the results of our latest post-editing productivity test, where we measured the productivity gain for translators post-editing MT output versus translating from scratch. Our analysis of the data indicates the presence of a strong correlation between the amount of editing applied to the raw MT output by the translators and their productivity gain. In addition, within the last calendar year our system has processed over thirteen million tokens of documentation content of which we have a record of the performed post-editing. This has allowed us to evaluate the performance of our MT engines for the different languages across our product portfolio, as well as spotlight potential issues with MT in the localisation process.

2010

pdf bib
Maximizing TM Performance through Sub-Tree Alignment and SMT
Ventsislav Zhechev | Josef van Genabith
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers

With the steadily increasing demand for high-quality translation, the localisation industry is constantly searching for technologies that would increase translator throughput, in particular focusing on the use of high-quality Statistical Machine Translation (SMT) supplementing the established Translation Memory (TM) technology. In this paper, we present a novel modular approach that utilises state-of-the-art sub-tree alignment and SMT techniques to turn the fuzzy matches from a TM into near-perfect translations. Rather than relegate SMT to a last-resort status where it is only used should the TM system fail to produce the desired output, for us SMT is an integral part of the translation process that we rely on to obtain high-quality results. We show that the presented system consistently produces better-quality output than the TM and performs on par or better than the standalone SMT system.

pdf bib
Highlighting matched and mismatched segments in translation memory output through sub-tree alignment
Ventsislav Zhechev
Proceedings of Translating and the Computer 32

pdf bib
Proceedings of the Second Joint EM+/CNGL Workshop: Bringing MT to the User: Research on Integrating MT in the Translation Industry
Ventsislav Zhechev
Proceedings of the Second Joint EM+/CNGL Workshop: Bringing MT to the User: Research on Integrating MT in the Translation Industry

pdf bib
Seeding Statistical Machine Translation with Translation Memory Output through Tree-Based Structural Alignment
Ventsislav Zhechev | Josef van Genabith
Proceedings of the 4th Workshop on Syntax and Structure in Statistical Translation

2008

pdf bib
Automatic Generation of Parallel Treebanks
Ventsislav Zhechev | Andy Way
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

pdf bib
Capturing translational divergences with a statistical tree-to-tree aligner
Mary Hearne | John Tinsley | Ventsislav Zhechev | Andy Way
Proceedings of the 11th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages: Papers

pdf bib
Robust language pair-independent sub-tree alignment
John Tinsley | Ventsislav Zhechev | Mary Hearne | Andy Way
Proceedings of Machine Translation Summit XI: Papers