AI-Powered Buddhist Translation - Community Roadmap

Trinley · April 30, 2025, 11:34am

Collaborative Strategy: AI-Powered Buddhist Translation

Version: 1.0

Date: April 30, 2025

Note: This collaborative planning document outlines our community’s approach to leveraging AI for translating and making Buddhist knowledge accessible. Please feel free to comment, suggest edits, or contribute directly to this document. Our goal is to build effective, transparent, and scholar-informed tools together.

1. Introduction & Vision

Our practical objective is to develop and implement AI-powered tools and workflows designed specifically for Buddhist text translation. These tools will directly support human translators by improving efficiency and consistency, while ensuring the nuances of the texts are respected. This document outlines the strategic plan for the technical community collaborating on this development effort.

2. The Opportunity & Challenges

Opportunity: AI offers the potential to significantly accelerate the translation process, improve consistency, and make Buddhist wisdom more widely available than ever before.

Current Challenges: However, current general-purpose AI translation methods present significant hurdles for the specific needs of Buddhist texts, as identified by translators and scholars:

Lack of Transparency: AI often functions as a “black box,” not revealing the reasoning behind its translation choices.
Trustworthiness Concerns: Issues like AI “hallucination” and unknown training data sources undermine confidence in the output.
Inconsistent Terminology: Maintaining consistent translation of key terms across large texts is difficult for current AI.
Scalability & Usability: Existing tools lack integration and require cumbersome workflows, especially for non-programmers, hindering effective human-AI collaboration.

3. Strategic Pillars & Proposed Solutions

To address these challenges, we propose focusing on the following strategic areas, developing specific solutions within each:

Pillar 1: Transparent AI Translation

Goal: Make the AI’s translation process interpretable and verifiable.
Proposed Solution: Develop workflows that convert source texts into detailed “semantic interpretations” (including word-by-word glosses, sentence-level logical analysis with UCCA trees, section-level discourse analysis with RST trees). This interpretation layer serves as a verifiable basis for generating target language translations.

Pillar 2: Scholar-Guided AI Translation

Goal: Integrate deep subject-matter expertise into the AI process.
Proposed Solution: Create systems that leverage historical scholarly resources (commentaries, reference works) and actively incorporate input from contemporary scholars (comments, Q&A, teachings) to continuously refine the semantic interpretation and guide the AI.

Pillar 3: Terminology Standardization

Goal: Ensure consistent and accurate use of key terms.
Proposed Solution: Build AI-assisted workflows to extract terminology, analyze usage variations across texts and translations, provide context, rank suggestions, and incorporate human expert feedback to standardize terms within the semantic interpretation.

Pillar 4: Integrated Tooling (Pecha Translation Suite / Lopenling)

Goal: Create a seamless and efficient environment for human-AI collaboration.
Proposed Solution: Develop a suite of AI-powered editors specifically designed for each phase of the translation process (semantic interpretation, terminology management, translation generation, review), supporting batch processing and intuitive user interfaces for translators.

4. Action Items & Next Steps

(This section is for community contribution. Please add specific tasks, potential owners, timelines, and required resources under each pillar.)