@inproceedings{lei-etal-2019-revisit,
title = "Revisit Automatic Error Detection for Wrong and Missing Translation {--} A Supervised Approach",
author = "Lei, Wenqiang and
Xu, Weiwen and
Aw, Ai Ti and
Xiang, Yuanxin and
Chua, Tat Seng",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1087",
doi = "10.18653/v1/D19-1087",
pages = "942--952",
abstract = "While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues. To have a closer study of these issues and accelerate model development, we propose automatic detecting adequacy errors in MT hypothesis for MT model evaluation. To do that, we annotate missing and wrong translations, the two most prevalent issues for current neural machine translation model, in 15000 Chinese-English translation pairs. We build a supervised alignment model for translation error detection (AlignDet) based on a simple Alignment Triangle strategy to set the benchmark for automatic error detection task. We also discuss the difficulties of this task and the benefits of this task for existing evaluation metrics.",
}
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<abstract>While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues. To have a closer study of these issues and accelerate model development, we propose automatic detecting adequacy errors in MT hypothesis for MT model evaluation. To do that, we annotate missing and wrong translations, the two most prevalent issues for current neural machine translation model, in 15000 Chinese-English translation pairs. We build a supervised alignment model for translation error detection (AlignDet) based on a simple Alignment Triangle strategy to set the benchmark for automatic error detection task. We also discuss the difficulties of this task and the benefits of this task for existing evaluation metrics.</abstract>
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%0 Conference Proceedings
%T Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach
%A Lei, Wenqiang
%A Xu, Weiwen
%A Aw, Ai Ti
%A Xiang, Yuanxin
%A Chua, Tat Seng
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F lei-etal-2019-revisit
%X While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues. To have a closer study of these issues and accelerate model development, we propose automatic detecting adequacy errors in MT hypothesis for MT model evaluation. To do that, we annotate missing and wrong translations, the two most prevalent issues for current neural machine translation model, in 15000 Chinese-English translation pairs. We build a supervised alignment model for translation error detection (AlignDet) based on a simple Alignment Triangle strategy to set the benchmark for automatic error detection task. We also discuss the difficulties of this task and the benefits of this task for existing evaluation metrics.
%R 10.18653/v1/D19-1087
%U https://aclanthology.org/D19-1087
%U https://doi.org/10.18653/v1/D19-1087
%P 942-952
Markdown (Informal)
[Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach](https://aclanthology.org/D19-1087) (Lei et al., EMNLP-IJCNLP 2019)
ACL