Abstract
Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with little amount of DL data; and 2) evaluate, as the main methods and data sets focus on SL evaluation. To address the first issue, we present a document-aligned Japanese-English conversation corpus, including balanced, high-quality business conversation data for tuning and testing. As for the second issue, we manually identify the main areas where SL MT fails to produce adequate translations in lack of context. We then create an evaluation set where these phenomena are annotated to alleviate automatic evaluation of DL systems. We train MT models using our corpus to demonstrate how using context leads to improvements.- Anthology ID:
- 2020.wmt-1.74
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 639–645
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.74
- DOI:
- Bibkey:
- Cite (ACL):
- Matīss Rikters, Ryokan Ri, Tong Li, and Toshiaki Nakazawa. 2020. Document-aligned Japanese-English Conversation Parallel Corpus. In Proceedings of the Fifth Conference on Machine Translation, pages 639–645, Online. Association for Computational Linguistics.
- Cite (Informal):
- Document-aligned Japanese-English Conversation Parallel Corpus (Rikters et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.74.pdf
- Optional supplementary material:
- 2020.wmt-1.74.OptionalSupplementaryMaterial.pdf
- Video:
- https://slideslive.com/38939560
- Code
- tsuruoka-lab/AMI-Meeting-Parallel-Corpus
- Data
- Business Scene Dialogue, JParaCrawl
Export citation
@inproceedings{rikters-etal-2020-document,
title = "Document-aligned {J}apanese-{E}nglish Conversation Parallel Corpus",
author = "Rikters, Mat{\=\i}ss and
Ri, Ryokan and
Li, Tong and
Nakazawa, Toshiaki",
editor = {Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.74",
pages = "639--645",
abstract = "Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with little amount of DL data; and 2) evaluate, as the main methods and data sets focus on SL evaluation. To address the first issue, we present a document-aligned Japanese-English conversation corpus, including balanced, high-quality business conversation data for tuning and testing. As for the second issue, we manually identify the main areas where SL MT fails to produce adequate translations in lack of context. We then create an evaluation set where these phenomena are annotated to alleviate automatic evaluation of DL systems. We train MT models using our corpus to demonstrate how using context leads to improvements.",
}
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<abstract>Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with little amount of DL data; and 2) evaluate, as the main methods and data sets focus on SL evaluation. To address the first issue, we present a document-aligned Japanese-English conversation corpus, including balanced, high-quality business conversation data for tuning and testing. As for the second issue, we manually identify the main areas where SL MT fails to produce adequate translations in lack of context. We then create an evaluation set where these phenomena are annotated to alleviate automatic evaluation of DL systems. We train MT models using our corpus to demonstrate how using context leads to improvements.</abstract>
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%0 Conference Proceedings %T Document-aligned Japanese-English Conversation Parallel Corpus %A Rikters, Matīss %A Ri, Ryokan %A Li, Tong %A Nakazawa, Toshiaki %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %S Proceedings of the Fifth Conference on Machine Translation %D 2020 %8 November %I Association for Computational Linguistics %C Online %F rikters-etal-2020-document %X Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with little amount of DL data; and 2) evaluate, as the main methods and data sets focus on SL evaluation. To address the first issue, we present a document-aligned Japanese-English conversation corpus, including balanced, high-quality business conversation data for tuning and testing. As for the second issue, we manually identify the main areas where SL MT fails to produce adequate translations in lack of context. We then create an evaluation set where these phenomena are annotated to alleviate automatic evaluation of DL systems. We train MT models using our corpus to demonstrate how using context leads to improvements. %U https://aclanthology.org/2020.wmt-1.74 %P 639-645
Markdown (Informal)
[Document-aligned Japanese-English Conversation Parallel Corpus](https://aclanthology.org/2020.wmt-1.74) (Rikters et al., WMT 2020)
- Document-aligned Japanese-English Conversation Parallel Corpus (Rikters et al., WMT 2020)
ACL
- Matīss Rikters, Ryokan Ri, Tong Li, and Toshiaki Nakazawa. 2020. Document-aligned Japanese-English Conversation Parallel Corpus. In Proceedings of the Fifth Conference on Machine Translation, pages 639–645, Online. Association for Computational Linguistics.