@inproceedings{zhu-etal-2023-kg,
title = "{KG}-{IQES}: An Interpretable Quality Estimation System for Machine Translation Based on Knowledge Graph",
author = "Zhu, Junhao and
Zhang, Min and
Yang, Hao and
Peng, Song and
Wu, Zhanglin and
Jiang, Yanfei and
Qiu, Xijun and
Pan, Weiqiang and
Zhu, Ming and
Miaomiao, Ma and
Zhang, Weidong",
editor = "Yamada, Masaru and
do Carmo, Felix",
booktitle = "Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track",
month = sep,
year = "2023",
address = "Macau SAR, China",
publisher = "Asia-Pacific Association for Machine Translation",
url = "https://aclanthology.org/2023.mtsummit-users.15",
pages = "162--170",
abstract = "The widespread use of machine translation (MT) has driven the need for effective automatic quality estimation (AQE) methods. How to enhance the interpretability of MT output quality estimation is well worth exploring in the industry. From the perspective of the alignment of named entities (NEs) in the source and translated sentences, we construct a multilingual knowledge graph (KG) consisting of domain-specific NEs, and design a KG-based interpretable quality estimation (QE) system for machine translations (KG-IQES). KG-IQES effectively estimates the translation quality without relying on reference translations. Its effectiveness has been verified in our business scenarios.",
}
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<abstract>The widespread use of machine translation (MT) has driven the need for effective automatic quality estimation (AQE) methods. How to enhance the interpretability of MT output quality estimation is well worth exploring in the industry. From the perspective of the alignment of named entities (NEs) in the source and translated sentences, we construct a multilingual knowledge graph (KG) consisting of domain-specific NEs, and design a KG-based interpretable quality estimation (QE) system for machine translations (KG-IQES). KG-IQES effectively estimates the translation quality without relying on reference translations. Its effectiveness has been verified in our business scenarios.</abstract>
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%0 Conference Proceedings
%T KG-IQES: An Interpretable Quality Estimation System for Machine Translation Based on Knowledge Graph
%A Zhu, Junhao
%A Zhang, Min
%A Yang, Hao
%A Peng, Song
%A Wu, Zhanglin
%A Jiang, Yanfei
%A Qiu, Xijun
%A Pan, Weiqiang
%A Zhu, Ming
%A Miaomiao, Ma
%A Zhang, Weidong
%Y Yamada, Masaru
%Y do Carmo, Felix
%S Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track
%D 2023
%8 September
%I Asia-Pacific Association for Machine Translation
%C Macau SAR, China
%F zhu-etal-2023-kg
%X The widespread use of machine translation (MT) has driven the need for effective automatic quality estimation (AQE) methods. How to enhance the interpretability of MT output quality estimation is well worth exploring in the industry. From the perspective of the alignment of named entities (NEs) in the source and translated sentences, we construct a multilingual knowledge graph (KG) consisting of domain-specific NEs, and design a KG-based interpretable quality estimation (QE) system for machine translations (KG-IQES). KG-IQES effectively estimates the translation quality without relying on reference translations. Its effectiveness has been verified in our business scenarios.
%U https://aclanthology.org/2023.mtsummit-users.15
%P 162-170
Markdown (Informal)
[KG-IQES: An Interpretable Quality Estimation System for Machine Translation Based on Knowledge Graph](https://aclanthology.org/2023.mtsummit-users.15) (Zhu et al., MTSummit 2023)
ACL
- Junhao Zhu, Min Zhang, Hao Yang, Song Peng, Zhanglin Wu, Yanfei Jiang, Xijun Qiu, Weiqiang Pan, Ming Zhu, Ma Miaomiao, and Weidong Zhang. 2023. KG-IQES: An Interpretable Quality Estimation System for Machine Translation Based on Knowledge Graph. In Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track, pages 162–170, Macau SAR, China. Asia-Pacific Association for Machine Translation.