My Conversations with ChatGPT
Machine Translation (MT) and translations generated by AI have brought about a paradigm shift in the translation industry. The output of MT will continue to improve, especially when associated with clients’ translation memories and human editing. Language Service Providers are adapting and are including MT in their processes, when MT is applicable. While linguists will continue to be essential in the process, their role will shift from that of translators to validators of content generated by MT. At Excel Translations, we wanted to know what AI thinks about all of this and decided to have an open conversation on the matter with ChatGPT. I will regularly publish a question and ChatGPT’s response, uncensored.
QUESTION 153:
What language pair requires the most post editing?
Chat GPT’s Response:
The language pair that typically requires the most post-editing often depends on the linguistic and cultural differences between the source and target languages, as well as the quality of the initial machine translation (MT) system used. However, some language pairs are generally recognized for presenting more challenges in MT and consequently require more post-editing. These include:
Chinese to English (and vice versa): The significant structural differences, idiomatic expressions, and cultural nuances between Chinese and English often result in MT outputs that require substantial post-editing.
Japanese to English (and vice versa): Similar to Chinese, Japanese has complex sentence structures, context-dependent meanings, and a different writing system, making MT outputs often less accurate and requiring more post-editing.
Arabic to English (and vice versa): Arabic’s rich morphology, context-specific meanings, and script directionality issues (right-to-left) tend to make MT less reliable, necessitating extensive post-editing.
Korean to English (and vice versa): The syntactic and lexical differences between Korean and English also contribute to lower initial MT quality and higher post-editing needs.
Hindi to English (and vice versa): While somewhat easier than the above pairs, Hindi still presents challenges in terms of grammar, syntax, and cultural references, requiring considerable post-editing.
These pairs are particularly challenging due to their distinct linguistic features, which are difficult for MT systems to handle accurately. Therefore, human post-editing plays a crucial role in ensuring the quality and accuracy of translations in these language pairs.
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