Requesting a Feature for Cross-Cultural Literary Analysis in AI

Hello OpenAI Community,

First time poster, huge fan. :slight_smile: I recently encountered a limitation with AI when trying to match an English translation of Dante’s “Inferno” with its original Italian text. This challenge highlights a gap in AI’s current capabilities: bridging translated literature back to its source. Simple on its surface, this capability would vastly improve AI’s cross-cultural bridge building.

Feature Proposal:

I propose developing a feature that enables AI to analyze translations of classic texts and identify their corresponding original passages. For example, given a passage from an English version of “Inferno,” the AI would pinpoint the exact section in Dante’s Italian text. Try it. From to Nietzsche to the Odyssey, it cannot currently reverse-engineer a passage into its source language.


Such a feature would not only enhance AI’s understanding of language nuances but also serve as a tool for exploring cross-cultural biases in translations. It would be a significant step toward understanding how translation choices reflect cultural perspectives, helping us gain deeper insights into the global interpretation of literature.

This idea presents a fascinating approach to understanding human translation methods, biases, and potential errors. Here’s how this could be approached using a famous public domain book:

  1. Select a Widely Translated Book: Choosing a classic work that is in the public domain and has been translated into multiple languages is the first step. A book like “The Odyssey” by Homer or “Don Quixote” by Miguel de Cervantes could be ideal due to their historical significance and widespread translation.

  2. Gather Translations in Various Languages: The next step would be to compile translations of this book in as many languages as possible, focusing on those that are most commonly spoken or studied.

  3. Text Alignment and Analysis: Using machine learning techniques, align the texts to match sections or sentences across different translations with the original. This step would identify how different translations handle the same source material.

  4. Identifying Patterns and Deviations: The analysis could focus on how different languages and translators approach aspects like idiomatic expressions, cultural references, sentence structure, and tone. This would reveal patterns in translation choices, including potential biases or inaccuracies.

  5. Error Detection and Correction: By comparing multiple translations with the original, it might be possible to identify and correct errors that have persisted across translations, especially in older translations where modern linguistic understanding wasn’t available.

  6. Reporting Findings: The final step would involve compiling these findings into a report or database that highlights the translation trends, common deviations, biases, and potential errors. This resource would be invaluable for linguists, translators, and scholars.