@inproceedings{zhang-etal-2023-leveraging,
title = "Leveraging Multilingual Knowledge Graph to Boost Domain-specific Entity Translation of {C}hat{GPT}",
author = "Zhang, Min and
Liu, Limin and
Yanqing, Zhao and
Qiao, Xiaosong and
Chang, Su and
Zhao, Xiaofeng and
Zhu, Junhao and
Zhu, Ming and
Peng, Song and
Li, Yinglu and
Liu, Yilun and
Ma, Wenbing and
Piao, Mengyao and
Tao, Shimin and
Yang, Hao and
Jiang, Yanfei",
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.7",
pages = "77--87",
abstract = "Recently, ChatGPT has shown promising results for Machine Translation (MT) in general domains and is becoming a new paradigm for translation. In this paper, we focus on how to apply ChatGPT to domain-specific translation and propose to leverage Multilingual Knowledge Graph (MKG) to help ChatGPT improve the domain entity translation quality. To achieve this, we extract the bilingual entity pairs from MKG for the domain entities that are recognized from source sentences. We then introduce these pairs into translation prompts, instructing ChatGPT to use the correct translations of the domain entities. To evaluate the novel MKG method for ChatGPT, we conduct comparative experiments on three Chinese-English (zh-en) test datasets constructed from three specific domains, of which one domain is from biomedical science, and the other two are from the Information and Communications Technology (ICT) industry {---} Visible Light Communication (VLC) and wireless domains. Experimental results demonstrate that both the overall translation quality of ChatGPT (+6.21, +3.13 and +11.25 in BLEU scores) and the translation accuracy of domain entities (+43.2{\%}, +30.2{\%} and +37.9{\%} absolute points) are significantly improved with MKG on the three test datasets.",
}
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<abstract>Recently, ChatGPT has shown promising results for Machine Translation (MT) in general domains and is becoming a new paradigm for translation. In this paper, we focus on how to apply ChatGPT to domain-specific translation and propose to leverage Multilingual Knowledge Graph (MKG) to help ChatGPT improve the domain entity translation quality. To achieve this, we extract the bilingual entity pairs from MKG for the domain entities that are recognized from source sentences. We then introduce these pairs into translation prompts, instructing ChatGPT to use the correct translations of the domain entities. To evaluate the novel MKG method for ChatGPT, we conduct comparative experiments on three Chinese-English (zh-en) test datasets constructed from three specific domains, of which one domain is from biomedical science, and the other two are from the Information and Communications Technology (ICT) industry — Visible Light Communication (VLC) and wireless domains. Experimental results demonstrate that both the overall translation quality of ChatGPT (+6.21, +3.13 and +11.25 in BLEU scores) and the translation accuracy of domain entities (+43.2%, +30.2% and +37.9% absolute points) are significantly improved with MKG on the three test datasets.</abstract>
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%0 Conference Proceedings
%T Leveraging Multilingual Knowledge Graph to Boost Domain-specific Entity Translation of ChatGPT
%A Zhang, Min
%A Liu, Limin
%A Yanqing, Zhao
%A Qiao, Xiaosong
%A Chang, Su
%A Zhao, Xiaofeng
%A Zhu, Junhao
%A Zhu, Ming
%A Peng, Song
%A Li, Yinglu
%A Liu, Yilun
%A Ma, Wenbing
%A Piao, Mengyao
%A Tao, Shimin
%A Yang, Hao
%A Jiang, Yanfei
%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 zhang-etal-2023-leveraging
%X Recently, ChatGPT has shown promising results for Machine Translation (MT) in general domains and is becoming a new paradigm for translation. In this paper, we focus on how to apply ChatGPT to domain-specific translation and propose to leverage Multilingual Knowledge Graph (MKG) to help ChatGPT improve the domain entity translation quality. To achieve this, we extract the bilingual entity pairs from MKG for the domain entities that are recognized from source sentences. We then introduce these pairs into translation prompts, instructing ChatGPT to use the correct translations of the domain entities. To evaluate the novel MKG method for ChatGPT, we conduct comparative experiments on three Chinese-English (zh-en) test datasets constructed from three specific domains, of which one domain is from biomedical science, and the other two are from the Information and Communications Technology (ICT) industry — Visible Light Communication (VLC) and wireless domains. Experimental results demonstrate that both the overall translation quality of ChatGPT (+6.21, +3.13 and +11.25 in BLEU scores) and the translation accuracy of domain entities (+43.2%, +30.2% and +37.9% absolute points) are significantly improved with MKG on the three test datasets.
%U https://aclanthology.org/2023.mtsummit-users.7
%P 77-87
Markdown (Informal)
[Leveraging Multilingual Knowledge Graph to Boost Domain-specific Entity Translation of ChatGPT](https://aclanthology.org/2023.mtsummit-users.7) (Zhang et al., MTSummit 2023)
ACL
- Min Zhang, Limin Liu, Zhao Yanqing, Xiaosong Qiao, Su Chang, Xiaofeng Zhao, Junhao Zhu, Ming Zhu, Song Peng, Yinglu Li, Yilun Liu, Wenbing Ma, Mengyao Piao, Shimin Tao, Hao Yang, and Yanfei Jiang. 2023. Leveraging Multilingual Knowledge Graph to Boost Domain-specific Entity Translation of ChatGPT. In Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track, pages 77–87, Macau SAR, China. Asia-Pacific Association for Machine Translation.