Document-level Neural MT: A Systematic Comparison
António Lopes, M. Amin Farajian, Rachel Bawden, Michael Zhang, André F. T. Martins
Correct Metadata for
Abstract
In this paper we provide a systematic comparison of existing and new document-level neural machine translation solutions. As part of this comparison, we introduce and evaluate a document-level variant of the recently proposed Star Transformer architecture. In addition to using the traditional metric BLEU, we report the accuracy of the models in handling anaphoric pronoun translation as well as coherence and cohesion using contrastive test sets. Finally, we report the results of human evaluation in terms of Multidimensional Quality Metrics (MQM) and analyse the correlation of the results obtained by the automatic metrics with human judgments.- Anthology ID:
- 2020.eamt-1.24
- Volume:
- Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Lisboa, Portugal
- Editors:
- André Martins, Helena Moniz, Sara Fumega, Bruno Martins, Fernando Batista, Luisa Coheur, Carla Parra, Isabel Trancoso, Marco Turchi, Arianna Bisazza, Joss Moorkens, Ana Guerberof, Mary Nurminen, Lena Marg, Mikel L. Forcada
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 225–234
- Language:
- URL:
- https://aclanthology.org/2020.eamt-1.24/
- DOI:
- Bibkey:
- Cite (ACL):
- António Lopes, M. Amin Farajian, Rachel Bawden, Michael Zhang, and André F. T. Martins. 2020. Document-level Neural MT: A Systematic Comparison. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 225–234, Lisboa, Portugal. European Association for Machine Translation.
- Cite (Informal):
- Document-level Neural MT: A Systematic Comparison (Lopes et al., EAMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.eamt-1.24.pdf
Export citation
@inproceedings{lopes-etal-2020-document,
title = "Document-level Neural {MT}: A Systematic Comparison",
author = "Lopes, Ant{\'o}nio and
Farajian, M. Amin and
Bawden, Rachel and
Zhang, Michael and
Martins, Andr{\'e} F. T.",
editor = "Martins, Andr{\'e} and
Moniz, Helena and
Fumega, Sara and
Martins, Bruno and
Batista, Fernando and
Coheur, Luisa and
Parra, Carla and
Trancoso, Isabel and
Turchi, Marco and
Bisazza, Arianna and
Moorkens, Joss and
Guerberof, Ana and
Nurminen, Mary and
Marg, Lena and
Forcada, Mikel L.",
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.24/",
pages = "225--234",
abstract = "In this paper we provide a systematic comparison of existing and new document-level neural machine translation solutions. As part of this comparison, we introduce and evaluate a document-level variant of the recently proposed Star Transformer architecture. In addition to using the traditional metric BLEU, we report the accuracy of the models in handling anaphoric pronoun translation as well as coherence and cohesion using contrastive test sets. Finally, we report the results of human evaluation in terms of Multidimensional Quality Metrics (MQM) and analyse the correlation of the results obtained by the automatic metrics with human judgments."
}
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%0 Conference Proceedings %T Document-level Neural MT: A Systematic Comparison %A Lopes, António %A Farajian, M. Amin %A Bawden, Rachel %A Zhang, Michael %A Martins, André F. T. %Y Martins, André %Y Moniz, Helena %Y Fumega, Sara %Y Martins, Bruno %Y Batista, Fernando %Y Coheur, Luisa %Y Parra, Carla %Y Trancoso, Isabel %Y Turchi, Marco %Y Bisazza, Arianna %Y Moorkens, Joss %Y Guerberof, Ana %Y Nurminen, Mary %Y Marg, Lena %Y Forcada, Mikel L. %S Proceedings of the 22nd Annual Conference of the European Association for Machine Translation %D 2020 %8 November %I European Association for Machine Translation %C Lisboa, Portugal %F lopes-etal-2020-document %X In this paper we provide a systematic comparison of existing and new document-level neural machine translation solutions. As part of this comparison, we introduce and evaluate a document-level variant of the recently proposed Star Transformer architecture. In addition to using the traditional metric BLEU, we report the accuracy of the models in handling anaphoric pronoun translation as well as coherence and cohesion using contrastive test sets. Finally, we report the results of human evaluation in terms of Multidimensional Quality Metrics (MQM) and analyse the correlation of the results obtained by the automatic metrics with human judgments. %U https://aclanthology.org/2020.eamt-1.24/ %P 225-234
Markdown (Informal)
[Document-level Neural MT: A Systematic Comparison](https://aclanthology.org/2020.eamt-1.24/) (Lopes et al., EAMT 2020)
- Document-level Neural MT: A Systematic Comparison (Lopes et al., EAMT 2020)
ACL
- António Lopes, M. Amin Farajian, Rachel Bawden, Michael Zhang, and André F. T. Martins. 2020. Document-level Neural MT: A Systematic Comparison. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 225–234, Lisboa, Portugal. European Association for Machine Translation.