Check it out here: arxiv.org/pdf/2601.10925
This work is the result of an ongoing collaboration between @lecslab.bsky.social and @ltiatcmu.bsky.social. Many thanks to my collaborators including @alexispalmer.bsky.social @lindiatjuatja.bsky.social @gneubig.bsky.social, and others!
Because the model predicted glosses without exposing the underlying morphological segmentation, the predictions were uninterpretable and difficult to trust.
“Linguistically-motivated” techniques for data augmentation sounds good on paper, but is it worth the cost?
Excited to announce that the PolyGloss paper has been accepted to @aclmeeting.bsky.social!
Previously, we trained models to help in endangered language documentation workflows by automatically predicting interlinear glosses. But real-world user studies revealed crucial issues...
The models are available right now on HuggingFace! See usage instructions on our GitHub: t.co/cqRgURAKIA
Furthermore, we show that our model can quickly adapt to a new language via LoRA with minimal examples, making it a practical choice for real-world documentation.
Find out in our new paper, which my colleague Ray Groshan will present at ACL!!
arxiv.org/abs/2506.03593
Really excited about this work w/ my long-time collaborators at Boulder!
We address limitations in existing morphosyntactic annotation systems for digitally under-resourced languages and show how *jointly* predicting morphological segmentation helps with glossing performance
Our new model jointly predicts the segmentation and glosses, optimizing for accuracy on both tasks and alignment between them. Predictions are not only interpretable, but more accurate than our prior model!
Excited to be presenting my work with @teaywright.bsky.social at #COLING2025 next week in Abu Dhabi! Find us in poster session 6/E on Jan 22nd (11 AM in the atrium).
Paper: arxiv.org/abs/2412.17427
michael ginn
michael ginn
michael ginn
michael ginn
michael ginn
michael ginn
michael ginn
michael ginn
Maria Valentini
Excited to announce that the PolyGloss paper has been accepted to @aclmeeting.bsky.social!
Previously, we trained models to help in endangered language documentation workflows by automatically predicting interlinear glosses. But real-world user studies revealed crucial issues...
