Ask ChatGPT – August 31, 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 50:

What are some linguistic nuances in translating Shakespearean English into modern languages using machine translation, and how do contemporary machine translation models handle these intricacies?

Chat GPT’s Response:

Translating Shakespearean English into modern languages poses several linguistic nuances that challenge contemporary machine translation models:

1. Archaic Vocabulary and Syntax: Shakespearean English includes words and phrases that have fallen out of common usage. Contemporary machine translation models may struggle to accurately render these archaic terms, resulting in potentially inaccurate or awkward translations.
2. Metrical and Poetic Structure: Many of Shakespeare’s works are written in specific metrical and poetic structures, such as iambic pentameter. Preserving these structures in translation while maintaining coherence and meaning can be a complex task for machine translation.
3. Wordplay and Puns: Shakespeare was known for his extensive use of wordplay, puns, and double entendres. These linguistic devices rely heavily on the nuances of the English language and its historical context, making them difficult to translate accurately into other languages.
4. Cultural References: Shakespeare’s works often contain references to historical events, myths, and cultural contexts that might not be familiar to speakers of modern languages. Translating these references accurately requires cultural and historical knowledge that machine translation models might lack.
5. Elizabethan Grammar and Syntax: The grammatical rules and sentence structures of Shakespearean English differ from modern grammar. Translating these unique grammatical features while ensuring readability in the target language can be a challenge.
6. Idiomatic Expressions: Shakespeare’s works are replete with idiomatic expressions that might not have direct equivalents in other languages. Machine translation models might struggle to convey the intended meaning without altering the expression significantly.

Contemporary machine translation models, while advanced, still face difficulties when dealing with these Shakespearean nuances. Some approaches that models might use to handle these intricacies include:

1. Contextual Learning: Advanced models can use contextual information from the surrounding text to decipher the meanings of archaic words and phrases based on their context, improving translation accuracy.
2. Parallel Texts: Machine translation models can leverage existing translations of Shakespeare’s works to learn how specific archaic terms and idioms have been previously translated into the target language.
3. Cultural Knowledge Databases: Models can be trained with extensive cultural and historical knowledge to better understand references and context-specific elements in Shakespeare’s works.
4. Poetry and Meter Preservation: Some models might be designed to preserve poetic structures by incorporating rules of meter and rhythm into the translation process.
5. Post-Editing by Experts: Translators with expertise in both Shakespearean English and the target language can review and edit machine-generated translations to ensure accuracy and readability while maintaining the linguistic nuances.

Despite these approaches, translating Shakespearean English remains a complex task for machine translation due to the intricate interplay of language, culture, and historical context in his works.