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REVIEW 4 major objections 5 minor 34 references

Common Ground, Diverse Roots: The Difficulty of Classifying Common Examples in Spanish Varieties

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A classifier's confidence in its predicted label can surface Spanish sentences valid in more than one variety.

desk verdict The DSL-TL half gives real evidence that predicted-label confidence ranks common examples above random, and the Cuban dataset is a genuine first, but the Cuban evaluation conflates commonness with injected label noise and the paper overclaims downstream benefit. read the letter →

arxiv 2412.11750 v1 pith:AJK2Y6RL submitted 2024-12-16 cs.CL

Javier A. Lopetegui , Arij Riabi , Djamé Seddah This is my paper · ORCID
classification cs.CL
keywords SpanishvarietieslanguagevarietyidentificationcommonexamplestrainingdynamicsCubandatasetannotationconfidencescoringNLPfairness
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 aims to establish that a classifier's training dynamics—specifically its average confidence in the label it predicts—can identify Spanish sentences that are valid in more than one regional variety. This matters because 'common examples' are frequent in Spanish, especially between closely related varieties, and many current variety-identification datasets label each sentence with a single variety, which misclassifies these ambiguous cases and can bias downstream tasks. The method is tested on two domains, newswire and Twitter, and in both it ranks common examples above random. Along the way the paper contributes the first dataset aimed at Cuban and Caribbean Spanish variety identification, built from 1,762 manually annotated tweets.

What carries the argument

The central object is a modified Datamaps scorer that replaces gold-label confidence with predicted-label confidence: $\mathrm{DMmean\text{-}pred} = -\frac{1}{E}\sum_{e=1}^{E}\max_j p_{i,j,e}$, the negative average, over $E$ epochs, of the maximum class probability the model assigns to example $i$. A second score, $\mathrm{DMstd\text{-}pred} = \sqrt{\frac{1}{E}\sum_{e=1}^{E}(\max_j p_{i,j,e} + \mathrm{DMmean\text{-}pred})^2}$, tracks epoch-to-epoch variability of that same maximum. The load-bearing change from earlier training-dynamics work is that the metrics are computed on the predicted label rather than the gold label, so the scorer can flag examples the model never confidently settles on—the signature of a sentence that is valid in several varieties. A random scorer serves as the control baseline, and feature-attribution analysis is used to inspect what drives the errors.

What would settle it

Recompute the DMmean-pred ranking on a version of CUBAN SPVARIETY where the random binary labels assigned to common examples are flipped; if the precision of the top-500 ranked common examples stays essentially unchanged, the score is tracking the manufactured label noise, not the commonness, and the central claim fails.

Watch

Extended reading notes

Core claim

The paper claims that the difficulty a binary variety-identification model has with 'common examples'—sentences valid in more than one Spanish variety—leaves a trace in training dynamics that can be harvested automatically. Instead of scoring each example by confidence on its gold label, as earlier Datamaps work did, the authors score by the average maximum probability assigned to the predicted label across epochs (DMmean-pred), with higher scores meaning lower confidence; a companion score DMstd-pred measures epoch-to-epoch variability. On both the Spanish subset of DSL-TL (Argentina versus Spain) and a new Twitter corpus of Cuban versus non-Cuban Spanish, the confidence-based ranking places common examples ahead of a random ranking, and confidence consistently outperforms variability. The paper also presents its CUBAN SPVARIETY dataset, 1,762 tweets annotated by three native Cuban speakers with labels for Cuban, non-Cuban, and common examples, as the first dataset focused on identifying a Cuban or other Caribbean Spanish variety.

Load-bearing premise

The load-bearing premise is that a sentence's low confidence in the model's predicted label is driven by its being valid in more than one Spanish variety rather than by unrelated label noise or topic bias, yet in the Cuban dataset common examples were randomly assigned to one of two labels before training, manufacturing exactly such noise.

Editorial extensions

If this is right

  • Dataset builders can prioritize re-annotation of the highest-scoring examples instead of checking every sentence by hand.
  • The scorer can act as an automatic error signal for existing single-label variety datasets, flagging candidates for a 'both' or 'neither' class like the one DSL-TL introduced.
  • For Cuban Spanish, the new dataset makes it possible to measure how strongly topic words such as Cuba and SOSCuba drive variety predictions, and the error analysis shows those signals need to be controlled.
  • The appendix's results indicate that multi-class, one-binary-classifier-per-variety models substantially outperform single-label classification on both datasets, reinforcing the need to handle common examples explicitly.

Reading between the lines

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

  • The same confidence-ranking signal could serve as an acquisition function for active learning: label the highest-scoring examples first, since they carry the most ambiguity.
  • A direct test of the mechanism would compare the ranking against human disagreement rates: if DMmean-pred truly tracks commonness, its top-ranked sentences should be the ones annotators most often split on, a pattern the paper's partial-agreement statistics suggest but do not use as the ranking criterion.
  • In a multi-variety setting beyond binary pairs, training a classifier for each pair of varieties and combining the confidence scores could identify sentences valid across several varieties at once, extending the binary design to the broader annotations the dataset guidelines anticipate.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes a training-dynamics-based method for automatically detecting common examples (texts valid across multiple Spanish varieties) in variety-identification datasets. It adapts the Datamaps confidence and variability metrics to use the maximum predicted probability rather than the gold-label probability (Equations 1 and 2), and evaluates the resulting scorers (DMmean-pred and DMstd-pred) against a random baseline on two datasets: the Spanish subset of DSL-TL and a newly introduced CUBAN SPVARIETY dataset of 1,762 manually annotated tweets. The authors report that DMmean-pred outperforms the random baseline and DMstd-pred on both datasets, claim that this demonstrates the usefulness of predicted-label confidence for detecting common examples, and provide a SHAP-based error analysis. They also present CUBAN SPVARIETY as the first dataset for Cuban (Caribbean) Spanish variety identification.

Significance. If the central claim holds, the paper would provide a practical, low-cost tool for flagging ambiguous examples in variety-identification datasets, which is a real need given the high prevalence of common examples in Spanish and other languages. The new CUBAN SPVARIETY dataset is a potentially valuable resource, particularly because Caribbean Spanish varieties are underrepresented in NLP resources. The paper also makes a useful methodological contribution by distinguishing confidence-based from variability-based training-dynamics scorers and by performing a detailed error analysis that exposes topic bias. However, the evaluation design has two serious confounds that currently prevent the results from supporting the central claim, as detailed in the major comments: the random relabeling of common examples in the Cuban dataset and the conflation of 'both' and 'neither' labels in DSL-TL.

major comments (4)
  1. [Section 4.2, Section 5.2] The random assignment of common examples to either ES-CU or not-ES-CU before training (Section 4.2) injects label noise that is uncorrelated with linguistic commonness. Since DMmean-pred scores examples by low confidence in the predicted label, it will systematically flag the randomly relabeled common examples as hard, regardless of whether their language is genuinely ambiguous. The evaluation therefore cannot separate detection of commonness from detection of injected noise, and the reported gains on CUBAN SPVARIETY (Table 1) may be an artifact of this procedure. The paper acknowledges the 'increased ambiguity' (Section 5.2) but does not treat this as a threat to validity. This is load-bearing for RQ3, which claims cross-domain effectiveness. I recommend either removing the random-assignment step and evaluating on the original annotations (e.g., as a multi-label or three-class problem), or comparing against a control condition in which non-common examples are also randomly relabeled to show that the method detects commonness, not label noise.
  2. [Section 4.1, Figure 2(a)] The DSL-TL 'common examples' class appears to merge the 'both' and 'neither' annotations from the original DSL-TL dataset. The paper states that 'a third label—both or neither—was introduced' and later treats the resulting 'ES' class as common examples. 'Neither' examples are not valid across multiple varieties; they are examples that annotators could not attribute to either variety, which is a different phenomenon. Including them in the common-example class conflates ambiguity from overlap with ambiguity from lack of identifiable features, and it is unclear how much of the reported DSL-TL performance is driven by each. The authors should separate these two cases or justify why they belong in the same class; without this, the DSL-TL results do not cleanly measure common-example detection.
  3. [Section 6, Figure 7] The paper's own error analysis shows that topic words, especially 'Cuba' and 'SOSCuba', dominate the model's mistakes (about 67% of the top-500 errors contain the word 'Cuba', versus 33% in the whole dataset). This indicates that the low-confidence signal captured by DMmean-pred can reflect topic bias rather than genuine multi-variety validity, and the SHAP analysis of the lowest-ranked common examples (Figure 9b) shows examples confidently classified on topic or non-linguistic grounds. These observations directly challenge the core assumption in Section 3.1 that low confidence in the predicted label is driven by a sentence being valid in multiple varieties. To support the central claim, the authors should control for topic effects (e.g., by masking or replacing named entities and hashtags) and show that the ranking advantage persists on topic-balanced subsets.
  4. [Table 1, Section 5.2] The paper claims that 'the two Datamaps models significantly outperform the baseline' and that DMmean-pred 'consistently outperforms' DMstd-pred, but no significance tests are reported. Given the reported standard deviations (e.g., DMmean-pred 54.75 ± 1.8 vs. DMstd-pred 52.88 ± 3.00 on DSL-TL; 63.51 ± 2.56 vs. 61.97 ± 2.60 on CUBAN SPVARIETY), some differences may not be significant across the five seeds. The authors should provide paired significance tests (e.g., bootstrap or permutation tests over the ranked lists) or at least report per-seed results and effect sizes, so that the 'significantly outperform' claim is backed by evidence.
minor comments (5)
  1. [Appendix A, Table 2] Table 2 is captioned 'DSL-TL Overview' but reports statistics (#sentences 1762, #tokens 41374) that match the CUBAN SPVARIETY dataset rather than DSL-TL. This is likely a labeling error and should be corrected.
  2. [Section 4.2] The annotation description says 'not_able_to_identify' but the guidelines in Appendix B use 'unable_to_identify_variety'; the terminology should be harmonized.
  3. [Section 8] The Limitations section acknowledges the binary-classification focus and the single-region annotator pool, but it does not mention the random relabeling of common examples or the both/neither conflation in DSL-TL; these should be listed as limitations given their impact on the reported results.
  4. [Section 5.2] The sentence 'The performance difference between DMmean-pred and DMstd-pred is more pronounced for smaller values of N, particularly in precision' is based on Figures 5 and 6, but those figures are not referenced at the point of the claim; adding explicit references would improve readability.
  5. [References] The reference to 'Vaidya et al., 2024' in Section 1 is cited for language-specific models being more sensitive to regional variations, but the cited paper is about emotion detection in Hinglish and does not appear to support this specific claim; consider replacing with a more directly relevant citation.

Circularity Check

1 steps flagged · score 4.0 of 10

CUBAN SPVARIETY validation is partially circular because common examples were randomly assigned binary labels before training, so DMmean-pred's high ranking of them is forced by the injected noise; DSL-TL provides independent support.

  1. self definitional [Section 4.2 (CUBAN SPVARIETY construction); interpreted in Section 5.2]
    "In this case, we only have the annotations with the common examples information (i.e. not single label approach). Then, to simulate a real-world scenario with single labels, we randomly assigned each common example a label of either ES-CU or not-ES-CU."

    The ES (common) examples are, by construction, the ones that receive random binary labels. The model is then trained on these labels, and DMmean-pred is defined as the negative mean over epochs of the maximum predicted probability. Examples with random labels cannot be learned with high confidence, so they are forced to the top of the ranking. The reported improvement over the random baseline on CUBAN SPVARIETY therefore measures the model's ability to detect the injected label noise rather than an independent signal of linguistic commonness. The DSL-TL experiment is not circular because its common labels come from separate human annotations while training uses the original binary labels.

full rationale

The paper's central claim that predicted-label confidence identifies common examples is genuinely supported by the DSL-TL experiment: there, the model is trained on the original ES-ES/ES-AR labels, and the common-example gold labels are independent human annotations (the newly introduced 'both or neither' label). The ranking signal is therefore not derived from the target by construction. However, the CUBAN SPVARIETY experiment is confounded. Section 4.2 states that every common example was randomly assigned to ES-CU or not-ES-CU before training, manufacturing label noise that is exactly what the confidence-based scorer detects. Thus the high APS and precision on CUBAN SPVARIETY are partly guaranteed by the experimental setup, not by the phenomenon of commonness. Section 5.2 even acknowledges that 'common examples were identified in the first round and randomly assigned to Cuban or non-Cuban classes, increasing ambiguity.' The Section 6 error analysis further shows that topic words like 'Cuba' and 'SOSCuba' dominate model predictions, indicating that the method may be capturing generic difficulty or topic bias rather than variety-overlap commonness. These are validity concerns, and the random-labeling issue is a partial circularity: the training labels for the target class are generated from the target itself. Because the DSL-TL result is independent and the method is externally benchmarked there, the circularity is not total; a score of 4 reflects one dataset whose validation reduces by construction while the central claim retains independent support.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The method has no fitted parameters, but it rests on empirical assumptions about the relationship between training dynamics and linguistic ambiguity. The strongest assumptions are listed above.

assumptions (4)
  • domain assumption Common examples are harder for a single-label classifier to learn than variety-specific examples.
    The method rests on this premise; Figure 3 provides supporting evidence but only for two binary variety pairs.
  • domain assumption Low confidence in the predicted label is a reliable indicator of commonness, not just of overall difficulty or noise.
    This is the central scoring hypothesis. The error analysis in Section 6 shows topic words like "Cuba" also drive confidence, weakening the assumption.
  • ad hoc to paper Randomly assigning common examples to one of two labels in CUBAN SPVARIETY simulates a real single-label dataset.
    Section 4.2 describes this assignment. It creates artificial label noise that may inflate the method's apparent success on that dataset.
  • domain assumption The original DSL-TL labels are sufficiently reliable for model uncertainty to reflect variety ambiguity rather than annotation error.
    Section 4.1 notes human annotators reached only 54.9% accuracy on Spanish, so label noise may be confounded with commonness.

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Cite this review

Pith. "Pith review of Common Ground, Diverse Roots: The Difficulty of Classifying Common Examples in Spanish Varieties." pith.science (2026). https://pith.science/paper/AJK2Y6RL

@misc{pith2026241211750,
  author       = {Pith},
  title        = {Pith review of: Common Ground, Diverse Roots: The Difficulty of Classifying Common Examples in Spanish Varieties},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AJK2Y6RL}},
  note         = {Machine review of arXiv:2412.11750}
}
read the original abstract

Variations in languages across geographic regions or cultures are crucial to address to avoid biases in NLP systems designed for culturally sensitive tasks, such as hate speech detection or dialog with conversational agents. In languages such as Spanish, where varieties can significantly overlap, many examples can be valid across them, which we refer to as common examples. Ignoring these examples may cause misclassifications, reducing model accuracy and fairness. Therefore, accounting for these common examples is essential to improve the robustness and representativeness of NLP systems trained on such data. In this work, we address this problem in the context of Spanish varieties. We use training dynamics to automatically detect common examples or errors in existing Spanish datasets. We demonstrate the efficacy of using predicted label confidence for our Datamaps \cite{swayamdipta-etal-2020-dataset} implementation for the identification of hard-to-classify examples, especially common examples, enhancing model performance in variety identification tasks. Additionally, we introduce a Cuban Spanish Variety Identification dataset with common examples annotations developed to facilitate more accurate detection of Cuban and Caribbean Spanish varieties. To our knowledge, this is the first dataset focused on identifying the Cuban, or any other Caribbean, Spanish variety.

Figures

Figures reproduced from arXiv: 2412.11750 by the authors.

Figure 1
Figure 1. Common Example Identification in Language [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Datasets distributions. 4.2 CUBANSPVARIETY To our knowledge, the dataset is the first dataset for Cuban or any Caribbean Spanish variety identi￾fication. The dataset contains manually annotated tweets with variety information. We collected the data from the publicly available archive The Twit￾ter Stream Grab in the website archive.org. We 4 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. F1-score during training for common and non [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Precision versus recall curve that the model’s average confidence offers a more reliable signal for identifying common examples, while the variability-based approach (DMstd-pred) tracks changes that do not always correspond with common examples. We observe that the dif…
Figure 6
Figure 6. Figure 6: Precision and Recall versus Top-N instances [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Fraction of error instances containing the word [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Agreement index for error instances in Top-N [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: For each dataset, we analyze the last two common examples in the ranking obtained using [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]

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    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

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Reviewed August 11, 2026 · model on record in the stance chip above.