Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity

Santanu Pal, Sudip Kumar Naskar, Josef van Genabith

In: 26th International Conference on Computational Linguistics. International Conference on Computational Linguistics (COLING-2016) 26th December 11-16 Osak Japan Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers 2016.


In this paper we combine two strands of machine translation (MT) research: automatic postediting (APE) and multi-engine (system combination) MT. APE systems learn a target- languageside second stage MT system from the data produced by human corrected output of a first stage MT system, to improve the output of the first stage MT in what is essentially a sequential MT system combination architecture. At the same time, there is a rich research literature on parallel MT system combination where the same input is fed to multiple engines and the best output is selected or smaller sections of the outputs are combined to obtain improved translation output. In the paper we show that parallel system combination in the APE stage of a sequential MT-APE combination yields substantial translation improvements both measured in terms of automatic evaluation metrics as well as in terms of productivity.

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German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz