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        <title>Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity</title>
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        <namePart type="given">Santanu</namePart>
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    <name type="personal">
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    <abstract>In this paper we combine two strands of machine translation (MT) research: automatic post-editing (APE) and multi-engine (system combination) MT. APE systems learn a target-language-side 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 improvements measured in a post-editing experiment. We also show that system combination on the level of APE alignments yields further improvements. Overall our APE system yields statistically significant improvement of 5.9% relative BLEU over a strong baseline (English–Italian Google MT) and 21.76% productivity increase in a human post-editing experiment with professional translators.</abstract>
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%0 Conference Proceedings
%T Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity
%A Pal, Santanu
%A Naskar, Sudip Kumar
%A van Genabith, Josef
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F pal-etal-2016-multi
%X In this paper we combine two strands of machine translation (MT) research: automatic post-editing (APE) and multi-engine (system combination) MT. APE systems learn a target-language-side 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 improvements measured in a post-editing experiment. We also show that system combination on the level of APE alignments yields further improvements. Overall our APE system yields statistically significant improvement of 5.9% relative BLEU over a strong baseline (English–Italian Google MT) and 21.76% productivity increase in a human post-editing experiment with professional translators.
%U https://aclanthology.org/C16-1241
%P 2559-2570

Markdown (Informal)

[Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity](https://aclanthology.org/C16-1241) (Pal et al., COLING 2016)

• Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity (Pal et al., COLING 2016)

ACL

• Santanu Pal, Sudip Kumar Naskar, and Josef van Genabith. 2016. Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2559–2570, Osaka, Japan. The COLING 2016 Organizing Committee.