Limin Liu


2023

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Leveraging Multilingual Knowledge Graph to Boost Domain-specific Entity Translation of ChatGPT
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 | Yanfei Jiang
Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track

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.