Ask ChatGPT – September 12, 2023

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 54:

Will machine translation work if English is not part of the language pair?

Chat GPT’s Response:

Machine translation typically works best when one of the languages in the language pair is English or when English is one of the languages with a large amount of training data available. This is because English is one of the most widely used and well-supported languages for machine translation. When translating from or to languages that are less common or have limited available training data, the accuracy and quality of machine translation can be significantly lower.

If you’re dealing with a language pair that doesn’t include English, especially if one or both of the languages are less common or have limited resources available for machine translation, you may encounter the following challenges:

1. Limited availability: There might be fewer machine translation models and resources for less common languages, making it difficult to find suitable tools.
2. Lower accuracy: Machine translation systems perform best when they have a large and diverse dataset to train on. For less common languages, the training data may be limited, leading to less accurate translations.
3. Specialized models: Some machine translation systems are better at handling specific language pairs due to the availability of specialized models and resources. If your language pair is not well-supported, you might need to explore other options or consider human translation.
4. Contextual and cultural challenges: Languages often have contextual and cultural nuances that are challenging for machine translation systems to capture, especially when there is limited training data.

If you’re working with a language pair that doesn’t include English, it’s a good idea to consult with experts or use alternative methods like professional human translation, especially for critical or sensitive content. Human translators can better handle the nuances and complexities of less common languages.