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        <title>Comparing Chinese-English MT Performance Involving ChatGPT and MT Providers and the Efficacy of AI mediated Post-Editing</title>
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    <abstract>The recent introduction of ChatGPT has caused much stir in the translation industry because of its impressive translation performance against leaders in the industry. We review some ma-jor issues based on the BLEU comparisons of Chinese-to-English (C2E) and English-to-Chinese (E2C) machine translation (MT) performance by ChatGPT against a range of leading MT providers in mostly technical domains. Based on sample aligned sentences from a sizable bilingual Chinese-English patent corpus and other sources, we find that while ChatGPT perform better generally, it does not consistently perform better than others in all areas or cases. We also draw on novice translators as post-editors to explore a major component in MT post-editing: Optimization of terminology. Many new technical words, including MWEs (Multi-Word Expressions), are problematic because they involve terminological developments which must balance between proper encapsulation of technical innovation and conforming to past traditions . Drawing on the above-mentioned corpus we have been developing an AI mediated MT post-editing (MTPE) system through the optimization of precedent rendition distribution and semantic association to enhance the work of translators and MTPE practitioners.</abstract>
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%0 Conference Proceedings
%T Comparing Chinese-English MT Performance Involving ChatGPT and MT Providers and the Efficacy of AI mediated Post-Editing
%A Cady, Larry
%A Tsou, Benjamin
%A Lee, John
%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 cady-etal-2023-comparing
%X The recent introduction of ChatGPT has caused much stir in the translation industry because of its impressive translation performance against leaders in the industry. We review some ma-jor issues based on the BLEU comparisons of Chinese-to-English (C2E) and English-to-Chinese (E2C) machine translation (MT) performance by ChatGPT against a range of leading MT providers in mostly technical domains. Based on sample aligned sentences from a sizable bilingual Chinese-English patent corpus and other sources, we find that while ChatGPT perform better generally, it does not consistently perform better than others in all areas or cases. We also draw on novice translators as post-editors to explore a major component in MT post-editing: Optimization of terminology. Many new technical words, including MWEs (Multi-Word Expressions), are problematic because they involve terminological developments which must balance between proper encapsulation of technical innovation and conforming to past traditions . Drawing on the above-mentioned corpus we have been developing an AI mediated MT post-editing (MTPE) system through the optimization of precedent rendition distribution and semantic association to enhance the work of translators and MTPE practitioners.
%U https://aclanthology.org/2023.mtsummit-users.20
%P 205-216

Markdown (Informal)

[Comparing Chinese-English MT Performance Involving ChatGPT and MT Providers and the Efficacy of AI mediated Post-Editing](https://aclanthology.org/2023.mtsummit-users.20) (Cady et al., MTSummit 2023)

• Comparing Chinese-English MT Performance Involving ChatGPT and MT Providers and the Efficacy of AI mediated Post-Editing (Cady et al., MTSummit 2023)

ACL

• Larry Cady, Benjamin Tsou, and John Lee. 2023. Comparing Chinese-English MT Performance Involving ChatGPT and MT Providers and the Efficacy of AI mediated Post-Editing. In Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track, pages 205–216, Macau SAR, China. Asia-Pacific Association for Machine Translation.