DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues
Sheila Castilho, João Lucas Cavalheiro Camargo, Miguel Menezes, Andy Way
Correct Metadata for
Abstract
Recently, the Machine Translation (MT) community has become more interested in document-level evaluation especially in light of reactions to claims of “human parity”, since examining the quality at the level of the document rather than at the sentence level allows for the assessment of suprasentential context, providing a more reliable evaluation. This paper presents a document-level corpus annotated in English with context-aware issues that arise when translating from English into Brazilian Portuguese, namely ellipsis, gender, lexical ambiguity, number, reference, and terminology, with six different domains. The corpus can be used as a challenge test set for evaluation and as a training/testing corpus for MT as well as for deep linguistic analysis of context issues. To the best of our knowledge, this is the first corpus of its kind.- Anthology ID:
- 2021.wmt-1.63
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 566–577
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.63/
- DOI:
- Bibkey:
- Cite (ACL):
- Sheila Castilho, João Lucas Cavalheiro Camargo, Miguel Menezes, and Andy Way. 2021. DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues. In Proceedings of the Sixth Conference on Machine Translation, pages 566–577, Online. Association for Computational Linguistics.
- Cite (Informal):
- DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues (Castilho et al., WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.63.pdf
- Video:
- https://aclanthology.org/2021.wmt-1.63.mp4
- Code
- sheilacastilho/dela-project
Export citation
@inproceedings{castilho-etal-2021-dela,
title = "{DELA} Corpus - A Document-Level Corpus Annotated with Context-Related Issues",
author = "Castilho, Sheila and
Cavalheiro Camargo, Jo{\~a}o Lucas and
Menezes, Miguel and
Way, Andy",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.63/",
pages = "566--577",
abstract = "Recently, the Machine Translation (MT) community has become more interested in document-level evaluation especially in light of reactions to claims of {\textquotedblleft}human parity{\textquotedblright}, since examining the quality at the level of the document rather than at the sentence level allows for the assessment of suprasentential context, providing a more reliable evaluation. This paper presents a document-level corpus annotated in English with context-aware issues that arise when translating from English into Brazilian Portuguese, namely ellipsis, gender, lexical ambiguity, number, reference, and terminology, with six different domains. The corpus can be used as a challenge test set for evaluation and as a training/testing corpus for MT as well as for deep linguistic analysis of context issues. To the best of our knowledge, this is the first corpus of its kind."
}
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%0 Conference Proceedings %T DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues %A Castilho, Sheila %A Cavalheiro Camargo, João Lucas %A Menezes, Miguel %A Way, Andy %Y Barrault, Loic %Y Bojar, Ondrej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussa, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Kocmi, Tom %Y Martins, Andre %Y Morishita, Makoto %Y Monz, Christof %S Proceedings of the Sixth Conference on Machine Translation %D 2021 %8 November %I Association for Computational Linguistics %C Online %F castilho-etal-2021-dela %X Recently, the Machine Translation (MT) community has become more interested in document-level evaluation especially in light of reactions to claims of “human parity”, since examining the quality at the level of the document rather than at the sentence level allows for the assessment of suprasentential context, providing a more reliable evaluation. This paper presents a document-level corpus annotated in English with context-aware issues that arise when translating from English into Brazilian Portuguese, namely ellipsis, gender, lexical ambiguity, number, reference, and terminology, with six different domains. The corpus can be used as a challenge test set for evaluation and as a training/testing corpus for MT as well as for deep linguistic analysis of context issues. To the best of our knowledge, this is the first corpus of its kind. %U https://aclanthology.org/2021.wmt-1.63/ %P 566-577
Markdown (Informal)
[DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues](https://aclanthology.org/2021.wmt-1.63/) (Castilho et al., WMT 2021)
- DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues (Castilho et al., WMT 2021)
ACL
- Sheila Castilho, João Lucas Cavalheiro Camargo, Miguel Menezes, and Andy Way. 2021. DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues. In Proceedings of the Sixth Conference on Machine Translation, pages 566–577, Online. Association for Computational Linguistics.