How focusing on word-level alignment in translation can obscure important linguistic patterns and meaning
How adopting a shared protocol like LSP could streamline Bible translation tools while preserving editor diversity.
One of the major hurdles to the translation of the Bible is the assumption that one cannot translate the Bible by paying attention to the language alone.
Empowering communities to create Bible translations in their languages using AI-powered tools.
Using multimodal assets and consensus validation to enable scalable translation for low-resource languages.
AI is revolutionizing Bible translation, but lacks crucial data for low-resource languages. Our mission is to bridge this gap.
Adapting double-entry bookkeeping principles to ensure accuracy and clear mapping between source and target texts in translation.
This article argues for mapping semantic similarity between translations rather than relying on problematic token-to-token alignments.
In the context of a translation project, where we aim to utilize only the resources at hand, lets consider a simplified approach to the evaluation stage.
I'm excited about the potential of OpenAI's new GPT-4o mini for our Translator's Copilot project! Here's why it could revolutionize Bible translation.
Exploring language similarity metrics to improve machine translation for low-resource languages
Using statistical methods to detect linguistic anomalies in translations
A straightforward machine translation pipeline designed to tackle the challenges of languages with limited resources.
Creating a short novel that covers the major genres of the Bible in a minimal form, as a way to gather translation data for new, undocumented languages.
If you are working with Bible translation data, you know how challenging it can be to deal with different types of data sources, formats, and structures.
It ought to be possible to run a continuous multi-agent simulation to iteratively improve a draft translation. Here's how it could work.
A protocol extending LSP to enable language servers to process non-textual data like audio and video.
Is there a simpler approach to Bible translation? Drawing inspiration from Tesla's application of Occam's razor to self-driving cars.
The future of Bible translation increasingly involves the use of crowd-sourcing, Large Language Models (LLMs), and AI to enhance accuracy and efficiency.
Bible translation is shifting from centralized to decentralized models, marking a significant change in how translations are created and validated.
Linguistic structures are the formal patterns that realize linguistic meanings. In translation, it is the meanings that are translated; the structures are left behind.
Exploring how semantic similarity between source verses can be used to suggest and disambiguate translations from multiple possible options.
Examining translation quality through semantic analogy and its implications for AI-assisted Bible translation.
A novel approach to translation quality checking using GAN principles to evaluate AI-generated translations.
A method to improve translation precision using valid tokens from sample translation pairs, focusing on back-translation and token coverage.
Exploring alternative approaches to machine translation for low-resource languages, focusing on in-context learning and LLM-based predictions rather than traditional transfer learning methods.
Exploring how Large Language Models can be used to generate and localize translator's notes for Bible translation projects worldwide.
Exploring the possibility of using VS Code as a base for an AI-native Bible translation app, inspired by successful forks like Cursor.
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