A Hybrid Statistical and Rule-based Approach to Extremely Low-resource Machine Transliteration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Machine transliteration work has focused primarily on languages with large volumes of parallel corpus, and between language pairs whose orthographies are very different. In contrast, a large proportion of the world’s languages have vastly fewer resources and employ Roman-like alphabets often with large degrees of orthographic overlap with high-resource languages. We propose that machine transliteration between languages with few training examples can be accomplished by a noisy-channel-like statistical model captured in a human editable format with practical rule-based capabilities built-in. This hybrid approach allows users to take advantage of an algorithm to find and apply common transformations in context while providing rigorous control over the output. Effectiveness is evaluated on the Bible names translation matrix dataset of Wu et al. (2018), covering 591 languages that involve 590 names on average per language pair. Our approach slightly exceeds past results and explores several features targeted at benefiting the extremely low-resource language domain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it