Abstract
We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references (Bawden et al., 2020), extending experiments to the multilingual setting for the WMT2020 metrics shared task and for three base metrics. We compare their capacity to exploit up to 100 additional synthetic references. We find that gains are possible when using additional, automatically paraphrased references, although they are not systematic. However, segment-level correlations, particularly into English, are improved for all three metrics and even with higher numbers of paraphrased references.- Anthology ID:
- 2020.wmt-1.98
- 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:
- 887–894
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.98
- DOI:
- Bibkey:
- Cite (ACL):
- Rachel Bawden, Biao Zhang, Andre Tättar, and Matt Post. 2020. ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task. In Proceedings of the Fifth Conference on Machine Translation, pages 887–894, Online. Association for Computational Linguistics.
- Cite (Informal):
- ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task (Bawden et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.98.pdf
- Video:
- https://slideslive.com/38939673
Export citation
@inproceedings{bawden-etal-2020-parbleu,
title = "{P}ar{BLEU}: Augmenting Metrics with Automatic Paraphrases for the {WMT}{'}20 Metrics Shared Task",
author = {Bawden, Rachel and
Zhang, Biao and
T{\"a}ttar, Andre and
Post, Matt},
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-1.98",
pages = "887--894",
abstract = "We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references (Bawden et al., 2020), extending experiments to the multilingual setting for the WMT2020 metrics shared task and for three base metrics. We compare their capacity to exploit up to 100 additional synthetic references. We find that gains are possible when using additional, automatically paraphrased references, although they are not systematic. However, segment-level correlations, particularly into English, are improved for all three metrics and even with higher numbers of paraphrased references.",
}
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%0 Conference Proceedings %T ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task %A Bawden, Rachel %A Zhang, Biao %A Tättar, Andre %A Post, Matt %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 bawden-etal-2020-parbleu %X We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references (Bawden et al., 2020), extending experiments to the multilingual setting for the WMT2020 metrics shared task and for three base metrics. We compare their capacity to exploit up to 100 additional synthetic references. We find that gains are possible when using additional, automatically paraphrased references, although they are not systematic. However, segment-level correlations, particularly into English, are improved for all three metrics and even with higher numbers of paraphrased references. %U https://aclanthology.org/2020.wmt-1.98 %P 887-894
Markdown (Informal)
[ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task](https://aclanthology.org/2020.wmt-1.98) (Bawden et al., WMT 2020)
- ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task (Bawden et al., WMT 2020)
ACL
- Rachel Bawden, Biao Zhang, Andre Tättar, and Matt Post. 2020. ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task. In Proceedings of the Fifth Conference on Machine Translation, pages 887–894, Online. Association for Computational Linguistics.