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        <title>A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning</title>
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        <namePart type="given">Gonçalo</namePart>
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    <abstract>Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits. APE systems are usually trained by complementing human post-edited data with large, artificial data generated through back-translations, a time-consuming process often no easier than training a MT system from scratch. in this paper, we propose an alternative where we fine-tune pre-trained BERT models on both the encoder and decoder of an APE system, exploring several parameter sharing strategies. By only training on a dataset of 23K sentences for 3 hours on a single GPU we obtain results that are competitive with systems that were trained on 5M artificial sentences. When we add this artificial data our method obtains state-of-the-art results.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1292</identifier>
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%0 Conference Proceedings
%T A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
%A Correia, Gonçalo M.
%A Martins, André F. T.
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F correia-martins-2019-simple
%X Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits. APE systems are usually trained by complementing human post-edited data with large, artificial data generated through back-translations, a time-consuming process often no easier than training a MT system from scratch. in this paper, we propose an alternative where we fine-tune pre-trained BERT models on both the encoder and decoder of an APE system, exploring several parameter sharing strategies. By only training on a dataset of 23K sentences for 3 hours on a single GPU we obtain results that are competitive with systems that were trained on 5M artificial sentences. When we add this artificial data our method obtains state-of-the-art results.
%R 10.18653/v1/P19-1292
%U https://aclanthology.org/P19-1292
%U https://doi.org/10.18653/v1/P19-1292
%P 3050-3056

Markdown (Informal)

[A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning](https://aclanthology.org/P19-1292) (Correia & Martins, ACL 2019)

• A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning (Correia & Martins, ACL 2019)

ACL

• Gonçalo M. Correia and André F. T. Martins. 2019. A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3050–3056, Florence, Italy. Association for Computational Linguistics.