learn.ifs.name

PUMA: language tokens for low-resource multilingual ASR

Oukid et al., 2026, PUMA (Findings of ACL 2026)

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4 cards in “3.2 Training regimes” · whole deck

3.2 Training regimes

  1. What loss is PUMA trained with (Eq. 12)?
    CTC loss over the per-frame output distributions, with a blank token for frames that don't emit a character.
  2. What are PUMA's three training regimes?
    Monolingual: a separate model per language. Progressive: languages added one after another. Simultaneous: all languages trained together.
    • Oukid et al., 2026, PUMA (Findings of ACL 2026), §3.2, p. 374
  3. How does progressive training fight catastrophic forgetting?
    Each new stage starts from the previous weights and replays a small subset of data from the languages already learned. The paper doesn't say how much data is replayed or in what order languages are added.
    • Oukid et al., 2026, PUMA (Findings of ACL 2026), §§3.2, 4.3.2, pp. 374–375
  4. How is simultaneous training set up, and what risk does the paper note?
    Training on the union of all language datasets, \(D = \bigcup_l D_l\), with mini-batches sampled across languages. The risk is gradient interference between languages.
    • Oukid et al., 2026, PUMA (Findings of ACL 2026), §3.2, p. 374

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