Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages
Bibliographic record
Abstract
Pretrained multilingual language models have been shown to work well on many languages for a variety of downstream NLP tasks.However, these models are known to require a lot of training data.This consequently leaves out a huge percentage of the world's languages as they are under-resourced.Furthermore, a major motivation behind these models is that lower-resource languages benefit from joint training with higher-resource languages.In this work, we challenge this assumption and present the first attempt at training a multilingual language model on only low-resource languages.We show that it is possible to train competitive multilingual language models on less than 1 GB of text.Our model, named AfriBERTa, covers 11 African languages, including the first language model for 4 of these languages.Evaluations on named entity recognition and text classification spanning 10 languages show that our model outperforms mBERT and XLM-R in several languages and is very competitive overall.Results suggest that our "small data" approach based on similar languages may sometimes work better than joint training on large datasets with high-resource languages.Code, data and models are released at https://github. com/keleog/afriberta.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".