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pith:ERBMSKCV

pith:2026:ERBMSKCVHF5ETVX2DJW5NHOQD3

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Pretraining Language Models with Subword Regularization: An Empirical Study of BPE Dropout in Low-Resource NLP

Marcel Dunaiski, Ruan Visser, Trienko Grobler

Stochastic tokenization during both pretraining and fine-tuning yields the best results in low-resource NLP tasks.

arxiv:2605.13436 v1 · 2026-05-13 · cs.CL · cs.LG

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Claims

C1strongest claim

Across tasks, the best results are typically obtained when stochastic tokenization is applied during both pretraining and fine-tuning, whereas applying BPE dropout only during fine-tuning can underperform deterministic tokenization in smaller-data settings.

C2weakest assumption

That the downsampled subsets of high-resource languages and the chosen evaluation tasks sufficiently represent truly low-resource scenarios, and that the modest morphological alignment gains explain the performance benefits.

C3one line summary

Stochastic tokenization with BPE dropout during both pretraining and fine-tuning outperforms deterministic tokenization or fine-tuning-only dropout on low-resource NLP tasks.

References

34 extracted · 34 resolved · 1 Pith anchors

[1] [Adelaniet al., 2022 ] David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beuk- man, Chester Palen-Michel, Constantine Lignos, Je- sujoba O. Alabi, Shamsuddeen H. Muhamma 2022

[2] [Arnett and Bergen, 2025] Catherine Arnett and Ben- jamin K 2025

[3] Morphynet: A large multilin- gual database of derivational and inflectional morphol- ogy 2021

[4] BPE-knockout: Pruning pre-existing BPE tokenisers with backwards-compatible morpho- logical semi-supervision 2024

[5] [Cognettaet al., 2024 ] Marco Cognetta, Vil ´em Zouhar, and Naoaki Okazaki 2024

Receipt and verification

First computed	2026-05-18T02:44:47.101101Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2442c92855397a49d6fa1a6dd69dd01ed208fffb1703e0dd7cb1e5486fb175bb

Aliases

arxiv: 2605.13436 · arxiv_version: 2605.13436v1 · doi: 10.48550/arxiv.2605.13436 · pith_short_12: ERBMSKCVHF5E · pith_short_16: ERBMSKCVHF5ETVX2 · pith_short_8: ERBMSKCV

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ERBMSKCVHF5ETVX2DJW5NHOQD3 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2442c92855397a49d6fa1a6dd69dd01ed208fffb1703e0dd7cb1e5486fb175bb

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-05-13T12:31:04Z",
    "title_canon_sha256": "fe3fc4f0fe3e5638c42b357229d5dddc318e3b413ba0ca2cab87bff14dadd35a"
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