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REVIEW 2 major objections 1 minor 103 references

"Chi nas dal soch el sent de legn" -- Auditing Text Corpora for Lombard

T0 review · 2 major / 1 minor · reviewed 2026-06-28 · grok-4.3

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Pith's one-line read Web-scraped data for Lombard is mostly misidentified noise and skewed toward Western varieties.

desk verdict Lombard audit flags mostly noisy scraped data and Western variety skew, but the manual labeling lacks any reported validation. read the letter →

arxiv 2606.06349 v1 pith:D4I5YYS6 submitted 2026-06-04 cs.CL

Edoardo Signoroni , Pavel Rychlý This is my paper · ORCID
classification cs.CL
keywords Lombardlanguagetextcorporaidentificationrepresentationalbiasunder-resourcedlanguagesweb-scrapeddataorthographicvariationNLPdatasets
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper audits available parallel and monolingual corpora for Lombard, a language continuum spoken in Italy. It shows that the apparent abundance of web-scraped material collapses under inspection because most text is wrongly labeled as Lombard, consists of boilerplate, or contains non-linguistic content. The small amount of genuine Lombard text that remains displays inconsistent spelling systems and strong geographic imbalance, with Western varieties dominating while Eastern ones appear far less often. The authors conclude that simply collecting more scraped data will not solve the problem and that careful, variety-sensitive curation by local communities is required instead.

What carries the argument

Manual audit that checks language validity, identifies orthographic systems, and classifies regional varieties within the corpora.

What would settle it

An independent large-scale audit that finds the majority of the same web-scraped material is correctly identified Lombard with balanced representation of Eastern and Western varieties would falsify the central claims.

Watch

Extended reading notes

Core claim

Our analysis reveals that the perceived abundance of web-scraped data is an illusion, with massive datasets plagued by severe language misidentification, boilerplate text, and non-linguistic noise. Furthermore, we analyze the orthographic composition of the valid Lombard portions across web-scraped datasets, curated corpora, and benchmarks. Our findings show conflicting orthographical systems and severe representational bias across all corpora: high-quality data is heavily skewed towards Western Lombard varieties, with Eastern ones left on the margins. This underscores the need for variety-aware, community-driven data curation rather than purely quantity-driven scraping.

Load-bearing premise

The manual audit can correctly separate real Lombard text from noise and assign orthographies and regional labels without missing sources or introducing consistent bias.

Editorial extensions

If this is right

  • Most existing web-scraped Lombard datasets cannot be used directly for training or evaluation because the majority of their content is not Lombard.
  • Even the valid Lombard text in current resources follows conflicting spelling conventions that would interfere with model consistency.
  • High-quality Lombard data currently over-represents Western varieties and under-represents Eastern varieties.
  • Purely quantity-driven scraping will continue to reproduce the same misidentification and bias problems.
  • Community involvement in curation is necessary to achieve better variety coverage.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same misidentification and variety-bias pattern is likely to appear in scraped data for other under-resourced language continua.
  • NLP data pipelines that prioritize size over verification may systematically exclude minority dialects across many languages.
  • Future benchmarks for Lombard should include explicit checks for orthographic consistency and geographic balance.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper conducts a manual audit of parallel and monolingual corpora for Lombard (an under-resourced language continuum), finding that web-scraped datasets are dominated by language misidentification, boilerplate, and non-linguistic noise. It further reports conflicting orthographic systems in the valid Lombard portions and severe representational bias, with high-quality data skewed toward Western varieties while Eastern ones are underrepresented. The work concludes that variety-aware, community-driven curation is needed over quantity-driven scraping.

Significance. If the audit methodology proves robust, the results would usefully document concrete data-quality failures in web-scraped resources for a low-resource language and quantify orthographic and dialectal skews across corpus types. Such evidence could directly inform corpus-construction practices and benchmark design in multilingual NLP.

major comments (2)
  1. [Methods] Methods / Audit procedure: No inter-annotator agreement, annotation guidelines, sampling procedure for large scraped collections, or auditor background is reported. Because the central claims (misidentification rates, orthographic composition, and Western/Eastern bias) rest entirely on the reliability of this manual classification, the absence of validation metrics leaves the quantitative findings unsupported.
  2. [Results] Results / Quantitative findings: The abstract states that web-scraped data are “plagued by severe” noise and that high-quality data are “heavily skewed,” yet no exact percentages, sample sizes, or tables of misidentification rates per corpus are referenced. Without these figures it is impossible to judge whether the reported problems are load-bearing or marginal.
minor comments (1)
  1. [Title] The title is given only in Lombard; an English gloss or subtitle would improve accessibility for the broader NLP audience.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for these targeted comments on methodology and results presentation. Both points identify areas where the manuscript can be strengthened with additional transparency, and we will revise accordingly.

read point-by-point responses

  1. Referee: [Methods] Methods / Audit procedure: No inter-annotator agreement, annotation guidelines, sampling procedure for large scraped collections, or auditor background is reported. Because the central claims (misidentification rates, orthographic composition, and Western/Eastern bias) rest entirely on the reliability of this manual classification, the absence of validation metrics leaves the quantitative findings unsupported.

    Authors: We agree the audit procedure requires fuller documentation. The manual classification was performed by the first author (a native speaker of Western Lombard with formal training in Romance linguistics and prior experience annotating dialectal data). We will add a new subsection (likely 2.2) that (a) reproduces the annotation guidelines used for language identification, noise detection, and variety labeling, (b) describes the sampling procedure (stratified random samples of 500–1000 lines per corpus, with explicit handling of very large scraped collections), and (c) states the auditor’s background. Because only one annotator was involved, inter-annotator agreement statistics are not applicable; we will explicitly note this limitation and outline how future work could incorporate multiple annotators. revision: yes

  2. Referee: [Results] Results / Quantitative findings: The abstract states that web-scraped data are “plagued by severe” noise and that high-quality data are “heavily skewed,” yet no exact percentages, sample sizes, or tables of misidentification rates per corpus are referenced. Without these figures it is impossible to judge whether the reported problems are load-bearing or marginal.

    Authors: The body of the paper already contains per-corpus counts and qualitative breakdowns, but we accept that these are not presented in a compact, easily verifiable form. We will insert a new summary table (and accompanying text) that reports, for each corpus: total lines examined, exact misidentification rate, boilerplate/non-linguistic noise percentage, and the Western vs. Eastern variety split among the valid Lombard sentences. Sample sizes will be stated explicitly (e.g., “n = 800 lines audited from Common Crawl”). This will allow readers to assess the magnitude of the issues directly. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: purely empirical audit with no derivations or self-referential steps

full rationale

The paper conducts a manual audit of corpora for Lombard language without any mathematical derivations, equations, fitted parameters, or self-citations that form a load-bearing chain. The analysis relies on direct inspection of data sources, and the central claims are based on empirical observations rather than any closed-loop definitions or predictions. This is a standard empirical study with no circular reasoning present.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

This is an empirical audit study with no free parameters, axioms, or invented entities; all claims rest on the manual audit process and its representativeness.

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0 comments
Cite this review

Pith. "Pith review of "Chi nas dal soch el sent de legn" -- Auditing Text Corpora for Lombard." pith.science (2026). https://pith.science/paper/D4I5YYS6

@misc{pith2026260606349,
  author       = {Pith},
  title        = {Pith review of: "Chi nas dal soch el sent de legn" -- Auditing Text Corpora for Lombard},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D4I5YYS6}},
  note         = {Machine review of arXiv:2606.06349}
}
read the original abstract

Several of the world's languages are still under-resourced in terms of Natural Language Processing (NLP) tools. This is mostly due to the lack of high-quality datasets to train, develop, and evaluate systems and models for several tasks, such as Machine Translation (MT). We conduct a manual audit of the parallel and monolingual corpora available for Lombard, an under-resourced language continuum from Italy. Our analysis reveals that the perceived abundance of web-scraped data is an illusion, with massive datasets plagued by severe language misidentification, boilerplate text, and non-linguistic noise. Furthermore, we analyze the orthographic composition of the valid Lombard portions across web-scraped datasets, curated corpora, and benchmarks. Our findings show conflicting orthographical systems and severe representational bias across all corpora: high-quality data is heavily skewed towards Western Lombard varieties, with Eastern ones left on the margins. This underscores the need for variety-aware, community-driven data curation rather than purely quantity-driven scraping.

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed June 28, 2026 · model on record in the stance chip above.