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REVIEW 4 major objections 4 minor 1 cited by

ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper introduces ViQA-COVID, a 6,444-question-answer Vietnamese dataset for COVID-19 machine reading comprehension, and reports that XLM-R large is the strongest tested model.

desk verdict A genuinely new Vietnamese multi-span COVID-19 MRC dataset, but the paper's own statistics don't add up and the benchmark numbers rest on unverified preprocessing. read the letter →

arxiv 2504.21017 v2 pith:LKSNO5EW submitted 2025-04-21 cs.CL cs.LG

Hai-Chung Nguyen-Phung , Ngoc C. Lê , Van-Chien Nguyen , Hang Thi Nguyen , Thuy Phuong Thi Nguyen This is my paper · ORCID
classification cs.CLcs.LG
keywords VietnamesemachinereadingcomprehensionCOVID-19multi-spanextractionquestionansweringbenchmarkdatasetXLM-Rsequencetagging
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 claims to fill two gaps at once: no COVID-19 machine reading comprehension dataset existed for Vietnamese, and no Vietnamese dataset required models to extract multiple answer spans from one passage. ViQA-COVID is presented as the first multi-span extraction MRC dataset for Vietnamese, built from public health reports and news passages, with 6,444 question-answer pairs over 537 passages and about 21% of answers composed of multiple spans. The authors benchmark five models and report that XLM-R large reaches 85.97% F1 and 72.00% exact match on the test set, with single-span answers much easier than multi-span ones. The value, if the dataset is sound, is a reusable evaluation and training resource for Vietnamese health-domain question answering.

What carries the argument

The load-bearing mechanism is the multi-span extraction setup: instead of predicting a single start–end interval, the models are trained with a sequence-tagging head that labels each token B (begin), I (inside), or O (outside) an answer span, so multiple answers can be recovered from B and O tokens. Because most passages are longer than the 384/256-token model input limit, the input pipeline splits passages into overlapping features with a stride of 128, which is what the paper relies on to keep answer spans intact. This combination—B/I/O tagging plus sliding-window splitting—is what the paper uses to make a long-passage, multi-answer Vietnamese benchmark tractable for BERT-style encoders.

What would settle it

Re-annotate a random sample of the passages with independent annotators and compute inter-annotator agreement on the answer spans; if agreement is low (especially on multi-span answers), the reported benchmark numbers cannot be trusted as measures of model capability.

Watch

Extended reading notes

Core claim

The central claim is that ViQA-COVID is a valid, reusable benchmark for Vietnamese COVID-19 machine reading comprehension, and the first Vietnamese multi-span extraction MRC dataset. The dataset contains 6,444 question-answer pairs over 537 passages, with 21.0–21.3% of answers multi-span, 10–12% non-span (unanswerable), and the rest single-span. On this benchmark, XLM-R large outperforms the other four tested models, achieving 72.00% exact match and 85.97% F1 on the test set, confirming that cross-lingual pretraining transfers to Vietnamese health texts better than Vietnamese-only PhoBERT variants. The paper also analyzes error types, showing that multi-span questions and long sequences of dates, places, or people are the main sources of failure.

Load-bearing premise

The dataset's ground truth is taken as correct without a reported inter-annotator agreement measure, so if the manual answer spans are noisy or inconsistent, the model scores are not a valid measure of reading ability.

Editorial extensions

If this is right

  • Vietnamese health-domain question-answering systems can now be trained and evaluated on a COVID-19-specific benchmark instead of relying only on general-domain Vietnamese datasets.
  • Multi-span extraction becomes a measurable sub-task in Vietnamese NLP, with about 21% of answers requiring multiple spans, so progress on that specific challenge can be tracked.
  • The reported model ranking (XLM-R large best, then XLM-R base, then PhoBERT variants, then mBERT) gives practitioners a clear baseline hierarchy for future work on Vietnamese MRC.
  • Because the dataset includes unanswerable questions (about 10–12%), it also supports evaluation of a model's ability to abstain rather than hallucinate an answer.

Reading between the lines

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

  • If ViQA-COVID is released and maintained, it could become a standard low-resource Vietnamese reading benchmark, and its 21% multi-span share might push Vietnamese models toward span-set decoding rather than single-interval predictions.
  • The error analysis suggests that enumerations of dates, places, and people are the hardest cases; an extension would be a dataset split that deliberately oversamples those question types to stress-test models.
  • The same annotation pipeline of public-health reports plus expert-advised questions could transfer to other Vietnamese health topics, such as dengue or influenza, without redesigning the dataset format.
  • Because a non-Vietnamese-specific XLM-R large wins despite the competition, a testable follow-up is whether a Vietnamese-specific model trained with a span-set objective would close the multi-span gap.
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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 / 4 minor

Summary. The paper introduces ViQA-COVID, an extractive machine reading comprehension dataset for Vietnamese focused on COVID-19, constructed from CDC Vietnam and other reputable sources. The dataset contains 6,444 question-answer pairs over 537 passages, with roughly 20% of answers requiring multiple spans, and it is claimed to be the first multi-span extraction MRC dataset and the first COVID-19 MRC dataset for Vietnamese. The authors also report fine-tuning experiments with mBERT, PhoBERT-base/large, and XLM-R-base/large, concluding that XLM-R-large achieves the best test performance with 85.97% F1 and 72.00% EM. The paper includes dataset statistics, a description of the annotation process, an error analysis, and a discussion of the difficulty profile.

Significance. If the dataset and benchmark results are reliable, ViQA-COVID fills a real gap: there is no public COVID-19 MRC benchmark for Vietnamese, and a multi-span extraction dataset for Vietnamese would be a useful resource for low-resource NLP and health-domain QA. The paper provides a substantial annotation effort, documents question-type and answer-type distributions, reports experiments with four pretrained model families, and includes an error analysis that gives a concrete picture of the remaining challenges. The claimed contribution is therefore significant for the Vietnamese NLP community. The main value depends on the dataset being released and on the annotation quality and evaluation pipeline being verifiable, which the current manuscript does not fully establish.

major comments (4)
  1. [Section 4.2 and Section 4.4] The passage-splitting scheme does not guarantee that every gold answer span is contained in at least one input feature. With maximum feature length 384 (PhoBERT: 256) and stride 128, a span such as tokens 250 to 400 falls neither in window [0, 383] nor in window [256, 639]; the overlap region is only 128 tokens wide, so spans longer than the stride that straddle a split boundary are unreachable. The paper states that overlap handles answers at split positions, but it reports no check that every gold span is a substring of some feature, no count of skipped or truncated examples, and no special handling for multi-span answers whose individual spans cross boundaries. Since Table 3 indicates 475 passages in the "greater than or equal to 512 tokens" class, this is not a corner case; if any gold spans in the test set are unreachable, the EM/F1 values in Table 4 are not valid estimates of performance on ViQA-COVID as claimed.
  2. [Section 3.2, Tables 1 and 3] The passage totals in Tables 1 and 3 are inconsistent. Table 1 reports 537 passages (284 train + 139 dev + 114 test = 537), whereas Table 3's length distribution sums to 612 (335 train + 151 dev + 126 test = 612). Because Section 4.2 motivates the splitting procedure directly from Table 3, this discrepancy makes the preprocessing pipeline unverifiable. The authors should reconcile the two tables and state explicitly whether Table 3 counts original passages, split input features, or some other unit. Until this is resolved, the relationship between the reported dataset statistics and the actual experimental setup is unclear.
  3. [Section 3.1] No inter-annotator agreement is reported. The annotation process is described as creation and cross-checking by three CDC analysts with advice from two CDC experts, but no quantitative consistency measure, such as span-level agreement, Cohen's kappa, or a small-scale double-annotation study, is provided. Since the ground-truth spans are the basis for all reported benchmark scores, and since multi-span annotation is inherently more subjective than single-span annotation, the reliability of the labels is unverified. The authors should add an agreement statistic or a carefully described verification study; otherwise, the model scores in Table 4 cannot be distinguished from scores on noisy labels.
  4. [Section 4.3 and Section 5] The evaluation protocol for non-span and multi-span answers is underspecified. Table 1 shows that 10 to 12 percent of answers are non-span, and roughly 20 percent are multi-span, but the paper never states how EM and F1 are computed for these cases under the B/I/O tagging approach. For example, is a predicted span on an unanswerable question scored as 0, and how are partially overlapping sets of predicted spans aggregated into a single F1? Without this definition, the aggregate numbers in Table 4 cannot be reproduced or meaningfully compared with other MRC benchmarks. The authors should provide the exact scoring formula used for non-span and multi-span predictions.
minor comments (4)
  1. [Abstract and throughout] The manuscript contains numerous grammatical and typographical errors, such as "After two years of appearance," "a answer can include multi-span," and "Data was encrypted sensitive information." A thorough language edit would improve clarity.
  2. [Sections 4.2 and 4.4] Section 4.2 says the models' maximum input feature length is 512 tokens, while Section 4.4 states that the maximum feature length used is 384 (PhoBERT 256). These statements should be reconciled, and the choice of 384/256 with stride 128 should be justified in terms of answer length and model capacity.
  3. [Section 3.2 and Section 6] The paper says the dataset will be "publicly release[d] soon," but no URL or repository is provided. For a resource paper, a release link or a concrete availability statement is expected, and without public access the central contribution cannot yet be used or independently checked.
  4. [Section 5 and Table 4] The experimental results are reported from what appears to be a single run per model. No confidence intervals, multiple-seed standard deviations, or significance tests are given, so the smaller performance gaps, such as XLM-R-base versus XLM-R-large on multi-span F1 (77.83 vs. 79.10 on the test set), may not be statistically meaningful. Adding variance across seeds or a paired significance test would strengthen the comparative claim.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; benchmark results are empirical measurements on a newly constructed dataset, and the only self-citations are non-load-bearing related-work mentions.

full rationale

The paper's central claims are the creation of the ViQA-COVID dataset and the measured baseline performances on it. Neither claim is derived from an input that already contains the output: the annotation process (Section 3.1) constructs questions and answer spans from CDC and online sources independently of any model, and Table 4 reports directly measured EM and F1 scores for standard pretrained models. There is no fitted parameter that is renamed as a prediction, and the sequence-tagging multi-span approach is imported from an external source [20]. The self-citations [8] and [9] appear only in the related-work discussion as Vietnamese COVID-19 NER datasets and are not load-bearing for the dataset's construction, its firstness claim, or the benchmark numbers. The claim that ViQA-COVID is the first multi-span extraction MRC dataset for Vietnamese is a literature assertion rather than a derivation, so it cannot reduce to the paper's own assumptions. Other concerns, such as the absence of inter-annotator agreement statistics, the discrepancy between 537 passages in Table 1 and 612 rows in Table 3, and whether the sliding-window preprocessing of Section 4.2 preserves every gold span, are validity and correctness risks rather than instances of circularity. Accordingly, no specific circular step can be exhibited from the text, and the appropriate finding is a low score of 1, reflecting only the presence of minor non-load-bearing self-citations.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The paper's central claim is a resource claim plus measured benchmark scores. It rests on annotation correctness (assumed, not measured), on the adequacy of long-passage splitting for scoring, and on standard segmentation tooling. There are no quantitatively fitted parameters in the scientific sense; the listed hyperparameters are standard fine-tuning choices. No new entities are postulated.

free parameters (3)
  • learning rate = 5e-5
    Standard fine-tuning choice (Section 4.4); hand-picked and not swept, so its value is not fitted to the test set but it is an unexamined training choice.
  • max feature length / stride = 384 (PhoBERT 256) / 128
    Long-passage splitting parameters (Sections 4.2, 4.4). They determine how answers crossing split boundaries are scored and are chosen by hand without reported sensitivity analysis.
  • training epochs / batch size / weight decay = 30 / 32 / 0.01
    Training configuration (Section 4.4). The selected epoch for the reported test numbers is not stated, so the effective early-stopping rule is unspecified.
assumptions (3)
  • domain assumption Manual annotation by three CDC analysts with two CDC expert advisors yields correct answer spans without a measured inter-annotator agreement metric.
    Section 3.1 describes the annotation process but reports no IAA; the validity of every benchmark number depends on this unmeasured premise.
  • domain assumption Splitting passages into overlapping features with stride 128 preserves every answer span and scoring integrity.
    Section 4.2; most passages exceed the model max length (Table 3), and the two statistic tables disagree on passage counts, so this premise is load-bearing and unverified.
  • domain assumption RDRSegmenter word segmentation is correct for the passages and aligns with PhoBERT's tokenization.
    Section 4.2 uses RDRSegmenter from VnCoreNLP; segmentation errors would shift span boundaries and affect EM and F1.

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Cite this review

Pith. "Pith review of ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese." pith.science (2026). https://pith.science/paper/LKSNO5EW

@misc{pith2026250421017,
  author       = {Pith},
  title        = {Pith review of: ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LKSNO5EW}},
  note         = {Machine review of arXiv:2504.21017}
}
read the original abstract

After two years of appearance, COVID-19 has negatively affected people and normal life around the world. As in May 2022, there are more than 522 million cases and six million deaths worldwide (including nearly ten million cases and over forty-three thousand deaths in Vietnam). Economy and society are both severely affected. The variant of COVID-19, Omicron, has broken disease prevention measures of countries and rapidly increased number of infections. Resources overloading in treatment and epidemics prevention is happening all over the world. It can be seen that, application of artificial intelligence (AI) to support people at this time is extremely necessary. There have been many studies applying AI to prevent COVID-19 which are extremely useful, and studies on machine reading comprehension (MRC) are also in it. Realizing that, we created the first MRC dataset about COVID-19 for Vietnamese: ViQA-COVID and can be used to build models and systems, contributing to disease prevention. Besides, ViQA-COVID is also the first multi-span extraction MRC dataset for Vietnamese, we hope that it can contribute to promoting MRC studies in Vietnamese and multilingual.

Figures

Figures reproduced from arXiv: 2504.21017 by the authors.

Figure 1
Figure 1. Illustrating the sequence tagging approach for multi-span questions. In which, [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Nested Named-Entity Recognition on Vietnamese COVID-19: Dataset and Experiments

    cs.CL 2025-04 conditional novelty 6.0 of 10

    A manually annotated Vietnamese COVID-19 NER dataset with 11 entity types and up to four nesting levels, plus BiLSTM and PhoBERT baselines where PhoBERT-large-CRF with cross-sentence context achieved the highest F1.

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