PashtoCorp: A 1.25-Billion-Word Corpus, Evaluation Suite, and Reproducible Pipeline for Low-Resource Language Development

PashtoCorp: A 1.25-Billion-Word Corpus, Evaluation Suite, and Reproducible Pipeline for Low-Resource Language Development | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/pashtocorp-a-1-25-billion-word-corpus-evaluation-suite-and-reproducible-pipeline-for-low-resource-language-development

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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Canonical ID pashtocorp-a-1-25-billion-word-corpus-evaluation-suite-and-reproducible-pipeline-for-low-resource-language-development | Route /signal-canvas/pashtocorp-a-1-25-billion-word-corpus-evaluation-suite-and-reproducible-pipeline-for-low-resource-language-development

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/pashtocorp-a-1-25-billion-word-corpus-evaluation-suite-and-reproducible-pipeline-for-low-resource-language-development

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: PashtoCorp: A 1.25-Billion-Word Corpus, Evaluation Suite, and Reproducible Pipeline for Low-Resource Language Development

PDF: https://arxiv.org/pdf/2603.16354v1

Source count: Pending verification

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

Signal Canvas receipt window

Watch and verify: PashtoCorp: A 1.25-Billion-Word Corpus, Evaluation Suite, and Reproducible Pipeline for Low-Resource Language Development

/buildability/pashtocorp-a-1-25-billion-word-corpus-evaluation-suite-and-reproducible-pipeline-for-low-resource-language-development

Watchwatch

Subject: PashtoCorp: A 1.25-Billion-Word Corpus, Evaluation Suite, and Reproducible Pipeline for Low-Resource Language Development

Verdict

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
At 1.25B words across 2.81 million documents, PashtoCorp is 40x larger than the OSCAR Pashto subset and 83x larger than the previously largest dedicated Pashto corpus.
Implicationpartial
Explicitly stated in the abstract with clear numeric comparisons.
Verificationpartial
partial
Evidencepartial
Continued MLM pretraining of XLM-R-base on PashtoCorp reduces held-out perplexity by 25.1% (8.08->6.06).
Implicationpartial
Explicitly stated in the abstract with precise numeric results.
Verificationpartial
partial
Evidencepartial
On WikiANN Pashto NER, the pretrained model improves entity F1 by 10% relative (19.0%->21.0%) and reduces training variance nearly 7x.
Implicationpartial
Explicitly stated in the abstract with precise numeric results.
Verificationpartial
partial
Evidencepartial
A leave-one-out source ablation shows that Wikipedia (0.7% of documents) is the most critical source for NER: removing it alone reduces entity F1 by 47%.
Implicationpartial
Explicitly stated in the abstract with clear numeric evidence.
Verificationpartial
partial
Evidencepartial
The corpus is assembled from 39 sources spanning seven HuggingFace datasets and 32 purpose-built web scrapers, processed through a reproducible pipeline with Arabic-script tokenization, SHA-256 deduplication, and quality filtering.
Implicationpartial
Directly stated in the abstract describing the method.
Verificationpartial
partial
Evidencepartial
On Belebele Pashto reading comprehension, Gemma-3n achieves 64.6% accuracy, the first published LLM baseline for Pashto on this benchmark.
Implicationpartial
Directly stated in the abstract with a specific result and claim of being first.
Verificationpartial
partial
Evidencepartial
Pashto, a language spoken by 60 million people that remains severely underrepresented in NLP.
Implicationpartial
Directly stated in the abstract as context, though the 'severely underrepresented' claim is qualitative but strongly implied by the research gap being addressed.
Verificationpartial
partial
Evidencepartial
the largest gain appears at 50 training sentences (+27%), with PashtoCorp covering 97.9% of WikiANN entity vocabulary.
Implicationpartial
Explicitly stated in the abstract with specific numeric results.
Verificationpartial
partial

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PashtoCorp: A 1.25-Billion-Word Corpus, Evaluation Suite, and Reproducible Pipeline for Low-Resource Language Development

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