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 3:
How well does MT correlate with a human translation?
ChatGPT’s response:
The correlation between machine translation (MT) and a human translation can vary depending on several factors. These factors include the quality and complexity of the MT system, the language pair being translated, the domain or subject matter of the text, and the availability of relevant training data.
In certain cases, particularly for simpler and more straightforward texts, MT systems can produce translations that closely align with human translations. This is especially true for widely used languages with extensive training data, such as English, Spanish, or French. For such languages, MT systems like Google Translate or DeepL can provide relatively accurate translations for general content like news articles, product descriptions, or basic conversation.
However, as the complexity and specificity of the text increase, the correlation between MT and a human translation can diminish. MT systems may struggle with idiomatic expressions, cultural nuances, specialized terminology, or texts requiring creative or subjective interpretation. Human translators, with their linguistic expertise, contextual understanding, and cultural knowledge, are often better equipped to handle these challenges and produce more accurate and nuanced translations.
It’s important to note that even when MT output correlates well with a human translation, there can still be differences in style, tone, and overall quality. Human translators are able to adapt their translations to the intended audience and convey the message in a more natural and polished manner.
Overall, while MT has made significant progress and can provide useful translations in many scenarios, it is still not on par with the consistent quality and nuanced understanding of high-performing human translators, especially in complex or specialized domains.
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