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        <title>An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing</title>
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        <namePart type="given">Marcin</namePart>
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            <namePart type="family">Kondrak</namePart>
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    <abstract>In this work, we explore multiple neural architectures adapted for the task of automatic post-editing of machine translation output. We focus on neural end-to-end models that combine both inputs mt (raw MT output) and src (source language input) in a single neural architecture, modeling {mt, src} \rightarrow pe directly. Apart from that, we investigate the influence of hard-attention models which seem to be well-suited for monolingual tasks, as well as combinations of both ideas. We report results on data sets provided during the WMT-2016 shared task on automatic post-editing and can demonstrate that dual-attention models that incorporate all available data in the APE scenario in a single model improve on the best shared task system and on all other published results after the shared task. Dual-attention models that are combined with hard attention remain competitive despite applying fewer changes to the input.</abstract>
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%0 Conference Proceedings
%T An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing
%A Junczys-Dowmunt, Marcin
%A Grundkiewicz, Roman
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F junczys-dowmunt-grundkiewicz-2017-exploration
%X In this work, we explore multiple neural architectures adapted for the task of automatic post-editing of machine translation output. We focus on neural end-to-end models that combine both inputs mt (raw MT output) and src (source language input) in a single neural architecture, modeling {mt, src} \rightarrow pe directly. Apart from that, we investigate the influence of hard-attention models which seem to be well-suited for monolingual tasks, as well as combinations of both ideas. We report results on data sets provided during the WMT-2016 shared task on automatic post-editing and can demonstrate that dual-attention models that incorporate all available data in the APE scenario in a single model improve on the best shared task system and on all other published results after the shared task. Dual-attention models that are combined with hard attention remain competitive despite applying fewer changes to the input.
%U https://aclanthology.org/I17-1013
%P 120-129

Markdown (Informal)

[An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing](https://aclanthology.org/I17-1013) (Junczys-Dowmunt & Grundkiewicz, IJCNLP 2017)

• An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing (Junczys-Dowmunt & Grundkiewicz, IJCNLP 2017)

ACL

• Marcin Junczys-Dowmunt and Roman Grundkiewicz. 2017. An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 120–129, Taipei, Taiwan. Asian Federation of Natural Language Processing.