Towards the First Named Entity Recognition of Inuktitut for an Improved Machine Translation
Bibliographic record
Abstract
Named Entity Recognition is a crucial step to ensure good quality performance of several Natural Language Processing applications and tools, including machine translation and information retrieval. Moreover, it is considered as a fundamental module of many Natural Language Understanding tasks such as question-answering systems. This paper presents a first study on NER for an under-represented Indigenous Inuit language of Canada, Inuktitut, which lacks linguistic resources and large labeled data.Our proposed NER model for Inuktitut is built by transferring linguistic characteristics from English to Inuktitut, based on either rules or bilingual word embeddings. We provide an empirical study based on a comparison with the state of the art models and as well as intrinsic and extrinsic evaluations. In terms of Recall, Precision and F-score, the obtained results show the effectiveness of the proposed NER methods. Furthermore, it improved the performance of Inuktitut-English Neural Machine Translation.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".