Empowering AI Agents with Minimal Generalizable Translation Memory

translation ai

The Problem of Data Scarcity in Ultra-Low-Resource Languages

We would like to create and revise AI-generated drafts in ultra-low-resource languages. This task is challenging because all machine translation techniques rely on large parallel corpora, which are unavailable by definition for such languages.

To solve this data scarcity, it is necessary to seed some initial data, and this initial seed data needs to be ultra high quality (in fact, this is probably far more important than the quantity of data).

The Idea: Minimal Generalizable Translation Memory

Creating a minimal generalizable translation memory could be a powerful approach to streamline the translation process and empower AI agents to draft and revise translations more effectively. By identifying the most valuable and reusable phrases within a corpus, we can create a translation memory that provides high coverage and applicability across the entire body of text.

The goal of this idea is to enable AI agents to both generate and refine translations by harnessing the most valuable and reusable phrases within a corpus. The idea relies on maximizing coverage and applicability while minimizing redundancy. We want to create a core translation memory for a corpus that is concise, generalizable, and high-impact.

Theoretical Principles

There are two key concepts at play in this approach, generalizability and coverage.

The concept of generalizability is closely related to information density. You aim to identify the most valuable strings (n-grams and skip-grams) in a corpus for a high-coverage and highly generalizable translation memory. This involves quantifying each string's information content and its potential for reuse in different contexts.

Information-Theoretic Underpinnings

• Shannon's Information Theory: Provides a mathematical foundation for measuring information density, enabling calculation of each string's information content using Shannon's entropy formula.
• Kolmogorov Complexity: Helps evaluate string complexity, related to generalizability, using Kolmogorov complexity-inspired metrics to assess compressibility and potential for reuse.
• Data Compression: Techniques like Huffman coding, LZ77, BWT, GZip, etc. compress the corpus, with the resulting compression ratios serving as a proxy for generalizability (I've been using compression ratios for anomaly detection recently and find them both intuitive and fascinating).

Practical Approach

• N-gram and Skip-Gram Extraction: Extract n-grams and skip-grams using sliding windows or skip-gram algorithms.
• Frequency and Co-occurrence Analysis: Calculate frequency and co-occurrence patterns to inform the generalizability metric.
• Entropy and Complexity Calculations: Apply Shannon's entropy formula and Kolmogorov complexity-inspired metrics to quantify information density and generalizability.
• Selection and Optimization: Rank strings by generalizability scores, filter out those below a threshold, and optimize the selection process to maximize coverage and generalizability.
• Evaluation and Refining: Assess translation memory performance using metrics like BLEU, CHRF++, or COMET on a held-out test set (probably using a cipher text to control for LLMs just "knowing" the translation memory).

Conclusion

By combining these theoretical principles and practical mechanisms, we can develop a robust method for identifying the most valuable strings in a corpus and creating a high-coverage, highly generalizable translation memory, empowering AI agents to draft and revise translations more effectively.

One final thought: if translating a fully analyzed corpus such as the Bible, leveraging syntactic and especially semantic information would likely improve the cross-linguistic utility of the core phrases/strings selected to represent the corpus as translation memory. I'm looking forward to trying this with OpenText 2.0 data for the Greek NT!