Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings

Elizaveta Yankovskaya; Andre Tättar; Mark Fishel

doi:10.18653/v1/W18-6466

Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings

Elizaveta Yankovskaya, Andre Tättar, Mark Fishel

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Abstract

This paper describes the submissions of the team from the University of Tartu for the sentence-level Quality Estimation shared task of WMT18. The proposed models use features based on attention weights of a neural machine translation system and cross-lingual phrase embeddings as input features of a regression model. Two of the proposed models require only a neural machine translation system with an attention mechanism with no additional resources. Results show that combining neural networks and baseline features leads to significant improvements over the baseline features alone.

Anthology ID:: W18-6466
Volume:: Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Month:: October
Year:: 2018
Address:: Belgium, Brussels
Editors:: Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
Venue:: WMT
SIG:: SIGMT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 816–821
Language:
URL:: https://aclanthology.org/W18-6466/
DOI:: 10.18653/v1/W18-6466
Bibkey:
Cite (ACL):: Elizaveta Yankovskaya, Andre Tättar, and Mark Fishel. 2018. Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 816–821, Belgium, Brussels. Association for Computational Linguistics.
Cite (Informal):: Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings (Yankovskaya et al., WMT 2018)
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PDF:: https://aclanthology.org/W18-6466.pdf

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@inproceedings{yankovskaya-etal-2018-quality,
    title = "Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings",
    author = {Yankovskaya, Elizaveta  and
      T{\"a}ttar, Andre  and
      Fishel, Mark},
    editor = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Specia, Lucia  and
      Turchi, Marco  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-6466/",
    doi = "10.18653/v1/W18-6466",
    pages = "816--821",
    abstract = "This paper describes the submissions of the team from the University of Tartu for the sentence-level Quality Estimation shared task of WMT18. The proposed models use features based on attention weights of a neural machine translation system and cross-lingual phrase embeddings as input features of a regression model. Two of the proposed models require only a neural machine translation system with an attention mechanism with no additional resources. Results show that combining neural networks and baseline features leads to significant improvements over the baseline features alone."
}

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%0 Conference Proceedings
%T Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings
%A Yankovskaya, Elizaveta
%A Tättar, Andre
%A Fishel, Mark
%Y Bojar, Ondřej
%Y Chatterjee, Rajen
%Y Federmann, Christian
%Y Fishel, Mark
%Y Graham, Yvette
%Y Haddow, Barry
%Y Huck, Matthias
%Y Yepes, Antonio Jimeno
%Y Koehn, Philipp
%Y Monz, Christof
%Y Negri, Matteo
%Y Névéol, Aurélie
%Y Neves, Mariana
%Y Post, Matt
%Y Specia, Lucia
%Y Turchi, Marco
%Y Verspoor, Karin
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 October
%I Association for Computational Linguistics
%C Belgium, Brussels
%F yankovskaya-etal-2018-quality
%X This paper describes the submissions of the team from the University of Tartu for the sentence-level Quality Estimation shared task of WMT18. The proposed models use features based on attention weights of a neural machine translation system and cross-lingual phrase embeddings as input features of a regression model. Two of the proposed models require only a neural machine translation system with an attention mechanism with no additional resources. Results show that combining neural networks and baseline features leads to significant improvements over the baseline features alone.
%R 10.18653/v1/W18-6466
%U https://aclanthology.org/W18-6466/
%U https://doi.org/10.18653/v1/W18-6466
%P 816-821

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Markdown (Informal)

[Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings](https://aclanthology.org/W18-6466/) (Yankovskaya et al., WMT 2018)

Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings (Yankovskaya et al., WMT 2018)

ACL

Elizaveta Yankovskaya, Andre Tättar, and Mark Fishel. 2018. Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 816–821, Belgium, Brussels. Association for Computational Linguistics.