@inproceedings{geng-etal-2023-unify,
title = "Unify Word-level and Span-level Tasks: {NJUNLP}{'}s Participation for the {WMT}2023 Quality Estimation Shared Task",
author = "Geng, Xiang and
Lai, Zhejian and
Zhang, Yu and
Tao, Shimin and
Yang, Hao and
Chen, Jiajun and
Huang, Shujian",
editor = "Koehn, Philipp and
Haddow, Barry and
Kocmi, Tom and
Monz, Christof",
booktitle = "Proceedings of the Eighth Conference on Machine Translation",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wmt-1.71",
doi = "10.18653/v1/2023.wmt-1.71",
pages = "829--834",
abstract = "We introduce the submissions of the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task. Our team submitted predictions for the English-German language pair on all two sub-tasks: (i) sentence- and word-level quality prediction; and (ii) fine-grained error span detection. This year, we further explore pseudo data methods for QE based on NJUQE framework (https://github.com/NJUNLP/njuqe). We generate pseudo MQM data using parallel data from the WMT translation task. We pre-train the XLMR large model on pseudo QE data, then fine-tune it on real QE data. At both stages, we jointly learn sentence-level scores and word-level tags. Empirically, we conduct experiments to find the key hyper-parameters that improve the performance. Technically, we propose a simple method that covert the word-level outputs to fine-grained error span results. Overall, our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks by a considerable margin.",
}
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<abstract>We introduce the submissions of the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task. Our team submitted predictions for the English-German language pair on all two sub-tasks: (i) sentence- and word-level quality prediction; and (ii) fine-grained error span detection. This year, we further explore pseudo data methods for QE based on NJUQE framework (https://github.com/NJUNLP/njuqe). We generate pseudo MQM data using parallel data from the WMT translation task. We pre-train the XLMR large model on pseudo QE data, then fine-tune it on real QE data. At both stages, we jointly learn sentence-level scores and word-level tags. Empirically, we conduct experiments to find the key hyper-parameters that improve the performance. Technically, we propose a simple method that covert the word-level outputs to fine-grained error span results. Overall, our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks by a considerable margin.</abstract>
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%0 Conference Proceedings
%T Unify Word-level and Span-level Tasks: NJUNLP’s Participation for the WMT2023 Quality Estimation Shared Task
%A Geng, Xiang
%A Lai, Zhejian
%A Zhang, Yu
%A Tao, Shimin
%A Yang, Hao
%A Chen, Jiajun
%A Huang, Shujian
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F geng-etal-2023-unify
%X We introduce the submissions of the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task. Our team submitted predictions for the English-German language pair on all two sub-tasks: (i) sentence- and word-level quality prediction; and (ii) fine-grained error span detection. This year, we further explore pseudo data methods for QE based on NJUQE framework (https://github.com/NJUNLP/njuqe). We generate pseudo MQM data using parallel data from the WMT translation task. We pre-train the XLMR large model on pseudo QE data, then fine-tune it on real QE data. At both stages, we jointly learn sentence-level scores and word-level tags. Empirically, we conduct experiments to find the key hyper-parameters that improve the performance. Technically, we propose a simple method that covert the word-level outputs to fine-grained error span results. Overall, our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks by a considerable margin.
%R 10.18653/v1/2023.wmt-1.71
%U https://aclanthology.org/2023.wmt-1.71
%U https://doi.org/10.18653/v1/2023.wmt-1.71
%P 829-834
Markdown (Informal)
[Unify Word-level and Span-level Tasks: NJUNLP’s Participation for the WMT2023 Quality Estimation Shared Task](https://aclanthology.org/2023.wmt-1.71) (Geng et al., WMT 2023)
ACL