Denotation Ambiguity Scoring for Panlingual Lexical Translation Inference
Bibliographic record
Abstract
PanLex is a massive database of interlinked lemmas in over two thousand language varieties. Among the uses for a resource such as this is the performance of translation inference on novel translations to construct large ontologies and potentially derive statistically attested semantic universals. This is an area of research that has long relied on explicit lexicographic demarcations of multiple senses among words to infer novel translations, a design feature which is here impossible and perhaps undesired. Here is proposed a new method for measuring the cost of translation as a function of ambiguity, potentially reimagining the structure of PanLex and opening the door to its use in probabilistic inference tasks to search for novel translations. This method for measuring ambiguity and ranking attested translations is tested against the intuitions of human translators in two language varieties, English and Polish. Ultimately implicit methods of ambiguity ranking are found to be insufficient for sorting lexical entries, with no real correlation between the scoring function and the intuitions of respondents. However, at longer distance translation chains there is a chance that application of an implicit ambiguity cost metric may have merit. These results are then discussed in terms of potential confounds, and the pragmatic issues of conceiving of translation as a path search problem over a graph of linked lemmas.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".