KG-IQES: An Interpretable Quality Estimation System for Machine Translation Based on Knowledge Graph
Junhao Zhu, Min Zhang, Hao Yang, Song Peng, Zhanglin Wu, Yanfei Jiang, Xijun Qiu, Weiqiang Pan, Ming Zhu, Ma Miaomiao, Weidong Zhang
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.- Anthology ID:
- 2023.mtsummit-users.15
- Volume:
- Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track
- Month:
- September
- Year:
- 2023
- Address:
- Macau SAR, China
- Editors:
- Masaru Yamada, Felix do Carmo
- Venue:
- MTSummit
- SIG:
- Publisher:
- Asia-Pacific Association for Machine Translation
- Note:
- Pages:
- 162–170
- Language:
- URL:
- https://aclanthology.org/2023.mtsummit-users.15
- DOI:
- Cite (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.
- Cite (Informal):
- KG-IQES: An Interpretable Quality Estimation System for Machine Translation Based on Knowledge Graph (Zhu et al., MTSummit 2023)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2023.mtsummit-users.15.pdf