Proceedings of the Tenth Workshop on Noisy and User-generated Text
Bibliographic record
Abstract
Processing data from non-standard dialects links two lines of research: creating NLP tools that are robust to "noisy" inputs, and extending the coverage of NLP tools to underserved language communities.In this talk, I will describe ways in which processing dialect data differs from processing standard-language data, and discuss some of the current challenges in dialect NLP research.For instance, I will talk about strategies to mitigate the effect of infelicitous subword tokenization caused by ad-hoc pronunciation spellings.Additionally, I argue that we should not only consider how to tackle dialectal variation in NLP, but also why.To this end, I will highlight perspectives of some dialect speaker communities on which language technologies should (or should not) be able to process or produce dialectal in-or output.Bio: Verena Blaschke is a final-year PhD student at LMU Munich.She currently researches NLP for nonstandard dialects and other low-resource language varieties, investigating how robust language models are towards language variation (and how to make them more robust).Her research is supervised by Barbara Plank and co-supervised by Hinrich Schtze.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".