Abstract
An important aspect of machine translation is its evaluation, which can be achieved through the use of a variety of metrics. To compare these metrics, the workshop on statistical machine translation annually evaluates metrics based on their correlation with human judgement. Over the years, methods for measuring correlation with humans have changed, but little research has been performed on what the optimal methods for acquiring human scores are and how human correlation can be measured. In this work, the methods for evaluating metrics at both system- and segment-level are analyzed in detail and their shortcomings are pointed out.- Anthology ID:
- 2020.wmt-1.103
- 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:
- 928–933
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.103
- DOI:
- Bibkey:
- Cite (ACL):
- Peter Stanchev, Weiyue Wang, and Hermann Ney. 2020. Towards a Better Evaluation of Metrics for Machine Translation. In Proceedings of the Fifth Conference on Machine Translation, pages 928–933, Online. Association for Computational Linguistics.
- Cite (Informal):
- Towards a Better Evaluation of Metrics for Machine Translation (Stanchev et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.103.pdf
- Video:
- https://slideslive.com/38939548
Export citation
@inproceedings{stanchev-etal-2020-towards,
title = "Towards a Better Evaluation of Metrics for Machine Translation",
author = "Stanchev, Peter and
Wang, Weiyue and
Ney, Hermann",
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-1.103",
pages = "928--933",
abstract = "An important aspect of machine translation is its evaluation, which can be achieved through the use of a variety of metrics. To compare these metrics, the workshop on statistical machine translation annually evaluates metrics based on their correlation with human judgement. Over the years, methods for measuring correlation with humans have changed, but little research has been performed on what the optimal methods for acquiring human scores are and how human correlation can be measured. In this work, the methods for evaluating metrics at both system- and segment-level are analyzed in detail and their shortcomings are pointed out.",
}
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%0 Conference Proceedings %T Towards a Better Evaluation of Metrics for Machine Translation %A Stanchev, Peter %A Wang, Weiyue %A Ney, Hermann %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 stanchev-etal-2020-towards %X An important aspect of machine translation is its evaluation, which can be achieved through the use of a variety of metrics. To compare these metrics, the workshop on statistical machine translation annually evaluates metrics based on their correlation with human judgement. Over the years, methods for measuring correlation with humans have changed, but little research has been performed on what the optimal methods for acquiring human scores are and how human correlation can be measured. In this work, the methods for evaluating metrics at both system- and segment-level are analyzed in detail and their shortcomings are pointed out. %U https://aclanthology.org/2020.wmt-1.103 %P 928-933
Markdown (Informal)
[Towards a Better Evaluation of Metrics for Machine Translation](https://aclanthology.org/2020.wmt-1.103) (Stanchev et al., WMT 2020)
- Towards a Better Evaluation of Metrics for Machine Translation (Stanchev et al., WMT 2020)
ACL
- Peter Stanchev, Weiyue Wang, and Hermann Ney. 2020. Towards a Better Evaluation of Metrics for Machine Translation. In Proceedings of the Fifth Conference on Machine Translation, pages 928–933, Online. Association for Computational Linguistics.