ViSoLex: An Open-Source Repository for Vietnamese Social Media Lexical Normalization

Anh Thi-Hoang Nguyen; Dung Ha Nguyen; Kiet Van Nguyen

REVIEW 3 major objections 3 minor 14 references

ViSoLex: An Open-Source Repository for Vietnamese Social Media Lexical Normalization

T0 review · 3 major / 3 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Adding NSW detection as a second task improves Vietnamese lexical normalization, most sharply when diacritics are stripped.

desk verdict A useful open-source resource undermined by a self-inconsistent evaluation table — the multitask advantage claim needs fixing before the paper is publishable. read the letter →

arxiv 2501.07020 v1 pith:G7KSMVWW submitted 2025-01-13 cs.CL cs.AI

Anh Thi-Hoang Nguyen , Dung Ha Nguyen , Kiet Van Nguyen This is my paper · ORCID

classification cs.CLcs.AI

keywords lexicalnormalizationVietnamesesocialmedianon-standardwordsmultitasklearningweaksupervisionNSWdetectionopen-sourceNLP

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

ViSoLex is an open-source system for normalizing non-standard Vietnamese social media text. The paper's central claim is that training the normalizer to simultaneously detect non-standard words (NSWs) improves normalization accuracy, with the clearest gain on heavily degraded text: for ViSoBERT, F1 rises from 72.19% to 75.93% when all diacritics are removed. The system also offers a dictionary lookup service, with unknown NSWs outsourced to GPT-4o and then added to the dictionary. A sympathetic reader would care because informal Vietnamese spelling breaks downstream tools, and a reproducible, extendable normalizer can serve sentiment analysis, hate speech detection, and translation.

What carries the argument

The central object is a multitask lexical normalizer built on a shared encoder with two task heads: a token-classification head for NSW detection and a sequence-generation head for normalization. A Rule Attention Network acts as a teacher, weighting weak supervision rules derived from an NSW dictionary and regular expressions, and the total loss is $L_{\mathrm{total}} = \alpha L_{\mathrm{norm}} + \beta L_{\mathrm{NSW}}$. The weak-supervision loop follows the ASTRA-style self-training idea, letting the model learn from unlabeled data and adapt to the diverse, evolving NSW patterns in social media.

What would settle it

Run the multitask and single-task normalizers on a held-out set of raw, unmodified Vietnamese social media comments (not diacritic-stripped) and compare F1; if the multitask model's F1 is not higher on that natural data, the paper's central claim does not generalize beyond the synthetic corruption.

Watch

Extended reading notes

Core claim

The discovery, on the paper's own terms, is that multitask learning—coupling NSW detection with lexical normalization in a single weakly supervised model—yields better normalization than normalization alone. The supporting evidence is Table 1: for ViSoBERT, F1 improves from 72.19% to 75.93% at full diacritic removal and from 75.79% to 77.22% on unperturbed text, while BARTpho sees smaller gains of 0.34% and 0.25%. The authors interpret this as evidence that detecting what is non-standard helps the model decide how to standardize it, especially when the input is visually degraded.

Load-bearing premise

The paper assumes that randomly removing diacritics from a clean corpus produces text similar enough to real Vietnamese social media slang that F1 gains on this synthetic test will hold on natural posts.

Editorial extensions

If this is right

If the multitask gain is real, future versions of ViSoLex can treat NSW detection as a default auxiliary task rather than an optional extra.
The dictionary lookup with a GPT-4o fallback means the repository can grow its coverage without manual entry for every new slang term.
The weakly supervised, modular design implies researchers can retrain the system with their own labeled files, unlabeled data, and a custom NSW dictionary for other languages or domains.
Normalized Vietnamese output should improve downstream tasks such as sentiment analysis and hate speech detection, where non-standard words typically degrade performance.

Reading between the lines

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

The evaluation only synthesizes noise by dropping diacritics; real social media noise also includes phonetic spellings, abbreviations, typographic errors, and code-switching. Testing on naturally noisy data would clarify whether the +3.74 point gain transfers.
Because the largest gain appears in ViSoBERT, a model pretrained specifically on Vietnamese social media, the multitask benefit may depend heavily on the pretraining domain; a general-domain encoder may not show the same jump.
A minimally testable extension is to train the same multitask architecture on other low-resource languages where detection labels are easier to obtain than normalized-word labels, and check whether normalization F1 rises similarly.
The GPT-4o expansion loop creates an active-learning feedback cycle: each user query with an unseen NSW proposes a new dictionary entry, and curator approval is the only quality gate, which could be studied systematically.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

ViSoLex is a genuinely useful open-source resource for Vietnamese social media normalization, but the paper's central quantitative claim is not currently reliable. Table 1 has a direction error that the authors' own prose contradicts: for ViSoBERT at p=0.0, Accuracy drops from 95.42 to 95.08, yet the Improvement row prints ↑0.34. Section 4 then says 'both models see slight improvements' and Section 6 claims 'consistent improvements in F1-score and accuracy across multitask settings.' You can't read the table as printed and also believe those sentences. This may be a typographical slip, but the table is the only reported comparison between single-task and multitask normalizers, so the numerical basis for the main advantage claim is internally inconsistent.

What is actually new here is modest but real: the authors extend their own weak supervision framework (Nguyen et al. 2024b) with multitask learning, an NSW dictionary lookup, and a web UI, and they ship it all open-source with a clean modular design. That is an engineering contribution, not a new mechanism. For Vietnamese NLP, which is under-resourced, having a reusable normalization tool with training scripts and a customizable dictionary is valuable. Credit is due for releasing code, data pipelines, and a demonstration interface.

The soft spots beyond the table error are the usual ones for system papers: no confidence intervals or significance tests, no dataset sizes or NSW counts, and an evaluation that only perturbs a clean corpus by removing diacritics with probability p. That synthetic corruption is not real social media noise, which includes abbreviations, phonetic spellings, typos, and mixed scripts. So even if the multitask gain at p=1 were real, it might not transfer to actual user-generated text. The authors also compare only against their own prior model; external baselines like MoNoise or other Vietnamese normalizers are absent. Given that ViLexNorm and other datasets exist, a deeper comparison would have strengthened the paper.

The weaknesses are correctable. The tool itself likely works, and the multitask approach may well help; but as printed, the evidence for that is not self-consistent. This paper deserves a serious peer review, not a desk reject, because the resource is timely and reusable. The reviewer should ask the authors to fix the table, report variance or at least dataset details, broaden the evaluation to natural noise, and temper the 'consistent improvements' language. After that, it would be a solid system contribution.

Referee Report

3 major / 3 minor

Summary. ViSoLex is an open-source repository for Vietnamese social media lexical normalization, providing two services: NSW Lookup and Lexical Normalization. The lexical normalizer is trained with multitask learning (NSW detection plus normalization) under a weak supervision framework with a Rule Attention Network, building on the authors' prior single-task framework. The paper reports an evaluation comparing single-task and multitask versions of BARTpho and ViSoBERT under two diacritic-removal ratios (p=0 and p=1), using F1-score, Integrity Score, and Accuracy. The central claim is that multitask learning consistently improves normalization performance, especially for ViSoBERT at p=1 with a 3.74-point F1 gain.

Significance. If the claims hold, ViSoLex would be a useful open-source resource for Vietnamese NLP, particularly for downstream tasks like sentiment analysis and hate speech detection, where social-media noise is common. The repository's modular design and weak supervision approach address a genuine scarcity of labeled Vietnamese data. However, the paper's significance as a research contribution is currently limited: the evaluation is narrow (synthetic diacritic removal only), the quantitative evidence is internally inconsistent, and no comparison is made to existing normalization systems or external benchmarks.

major comments (3)

[Table 1, Section 4] Table 1 contains a sign error in the ViSoBERT Accuracy row for p=0.0: single-task accuracy is 95.42% and multitask accuracy is 95.08%, a decrease of 0.34 percentage points, yet the Improvement row prints ↑0.34. This directly contradicts the text in Section 4 that "both models see slight improvements" and Section 6 that ViSoLex "achieves consistent improvements in F1-score and accuracy across multitask settings." Since Table 1 is the only reported quantitative comparison, this inconsistency undermines the internal reliability of the central claim and must be corrected and re-verified, ideally with raw prediction outputs or a reproducible script.
[Section 4] No confidence intervals, significance tests, dataset sizes, or counts of non-standard words (NSWs) are reported. Improvements of 0.08–0.38 percentage points in Accuracy for most conditions may be within normal evaluation variance, especially without any measure of variability across runs or test-set partitions. The paper should report the number of sentences/tokens in the test set, the number of NSW instances, and ideally multiple seeded runs with confidence intervals, so readers can assess whether the claimed multitask advantage is statistically distinguishable from noise.
[Section 4, Section 6] The evaluation protocol only perturbs a clean corpus by removing diacritics with probability p. Real Vietnamese social media noise includes abbreviations, phonetic spellings, typos, misspellings, and mixed scripts, which this synthetic corruption does not capture. The largest reported gain (ViSoBERT F1 +3.74 at p=1) is essentially a diacritic-restoration scenario, and thus may not transfer to natural NSWs. The authors should evaluate on a natural NSW benchmark such as ViLexNorm or on a held-out set of authentic social media text, or at least provide a qualitative error analysis showing which types of NSWs are handled by the multitask model.

minor comments (3)

[Section 4] Typo: "with diacritics removal in traning and development dataset" should read "training"; also "Table 1 demonstrate" should be "Table 1 demonstrates."
[Section 3.2.1] Equation (1) introduces loss-balancing weights α and β, but the paper never reports the values used in the experiments or any sensitivity analysis; please state the chosen hyperparameters and whether they were tuned.
[Section 3.2.2] The contribution of the Rule Attention Network is not isolated experimentally; the reported multitask gains conflate the effect of multitask learning with the effect of weak-supervision rules. An ablation removing the RAN would clarify which component drives the improvement.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the multitask improvement claim is an empirical ablation against the authors' own prior system, with no equation reducing to a fitted value or to a self-citation.

full rationale

This is a systems paper rather than a derivation. The central claim that multitask learning improves lexical normalization is supported by Table 1, which compares a multitask model with a single-task baseline. The baseline comes from the authors' previous work (Nguyen et al. 2024b) and the metrics are cited to that paper, but the comparison is an empirical ablation with concrete numbers, not an argument that assumes the conclusion. The weak supervision rules are derived from an NSW dictionary built via GPT-4o, and the teacher-student setup follows the external ASTRA framework; none of these inputs are defined in terms of the multitask model's output. The dictionary lookup and normalization services are independent functionalities. Self-citations to ViSoBERT, BARTpho, and prior datasets supply models and corpora, not the conclusion itself. The Table 1 sign inconsistency for ViSoBERT Accuracy at p=0.0 (95.42 vs 95.08 printed as an improvement of 0.34) is a correctness or reporting concern that a reader should verify, but it is not circularity: no fitted parameter is relabeled as a prediction and no equation reduces to itself. The lack of external benchmarks weakens external validity but does not make the claim circular, so the appropriate score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

No new physical or conceptual entities are postulated; the system combines existing model architectures and rules.

free parameters (2)

Loss balance weights alpha and beta = not reported
Eq. (1) defines total loss as alpha*L_norm + beta*L_NSW, but the paper gives no values, so the reported results depend on unreported hyperparameters.
Diacritics removal ratio p = 0.0 and 1.0
The evaluation uses synthetic diacritic removal at p=0 and p=1 (Table 1); this manual choice determines the difficulty of the test setting.

assumptions (3)

domain assumption The VN-NSW dictionary and regular expression rules provide reliable weak supervision signals.
Section 3.2.2 states weak rules are 'derived from NSW dictionary and regular expression rules,' but no analysis of rule coverage or precision is provided.
domain assumption Pre-trained BARTpho and ViSoBERT base models are appropriate for the normalization task and carry no systematic bias for this domain.
Section 3.2.1 fine-tunes these models, relying on their pretraining distributions without additional validation.
domain assumption GPT-4o-generated dictionary definitions and examples are accurate enough for lookup and weak supervision.
Section 3.1.1 says definitions are generated via the OpenAI GPT-4o API, but no quality audit or human verification is reported.

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Cite this review

Pith. "Pith review of ViSoLex: An Open-Source Repository for Vietnamese Social Media Lexical Normalization." pith.science (2026). https://pith.science/paper/G7KSMVWW

@misc{pith2026250107020,
  author       = {Pith},
  title        = {Pith review of: ViSoLex: An Open-Source Repository for Vietnamese Social Media Lexical Normalization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G7KSMVWW}},
  note         = {Machine review of arXiv:2501.07020}
}

read the original abstract

ViSoLex is an open-source system designed to address the unique challenges of lexical normalization for Vietnamese social media text. The platform provides two core services: Non-Standard Word (NSW) Lookup and Lexical Normalization, enabling users to retrieve standard forms of informal language and standardize text containing NSWs. ViSoLex's architecture integrates pre-trained language models and weakly supervised learning techniques to ensure accurate and efficient normalization, overcoming the scarcity of labeled data in Vietnamese. This paper details the system's design, functionality, and its applications for researchers and non-technical users. Additionally, ViSoLex offers a flexible, customizable framework that can be adapted to various datasets and research requirements. By publishing the source code, ViSoLex aims to contribute to the development of more robust Vietnamese natural language processing tools and encourage further research in lexical normalization. Future directions include expanding the system's capabilities for additional languages and improving the handling of more complex non-standard linguistic patterns.

Figures

Figures reproduced from arXiv: 2501.07020 by the authors.

**Figure 1.** The Architecture of ViSoLex. The diagram illustrates the modular components enabling NSW Lookup and Lexical Normalization services, including their interactions and flow of user inputs. 3.1.1 NSW LookUp Service The NSW LookUp service enables users to retrieve potential standard forms and interpretations of NSWs from an established dictionary. Upon choosing this service, users are asked to input an NSW, which is th… view at source ↗

**Figure 2.** Weak Supervision Training. This figure il [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** User Interface of NSW LookUp Service. This [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: User Interface of Lexical Normalization Ser [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

14 extracted references · 11 canonical work pages

[1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...
[2]

write newline

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[3]

Ciosici and Ira Assent

Manuel R. Ciosici and Ira Assent. 2018. https://aclanthology.org/C18-2001 Abbreviation expander - a web-based system for easy reading of technical documents . In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 1--4, Santa Fe, New Mexico. Association for Computational Linguistics

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Dinh-Truong Do, Ha Thanh Nguyen, Thang Ngoc Bui, and Hieu Dinh Vo. 2021. Vsec: Transformer-based model for vietnamese spelling correction. In PRICAI 2021: Trends in Artificial Intelligence, pages 259--272, Cham. Springer International Publishing

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work page doi:10.18653/v1/2021.naacl-main.66 2021
[6]

Benjamin Muller, Benoit Sagot, and Djam \'e Seddah. 2019. https://doi.org/10.18653/v1/D19-5539 Enhancing BERT for lexical normalization . In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 297--306, Hong Kong, China. Association for Computational Linguistics

work page doi:10.18653/v1/d19-5539 2019
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Anh Thi-Hoang Nguyen, Dung Ha Nguyen, Nguyet Thi Nguyen, Khanh Thanh-Duy Ho, and Kiet Van Nguyen. 2024 a . Automatic textual normalization for hate speech detection. In Intelligent Systems Design and Applications, pages 1--12, Cham. Springer Nature Switzerland

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Dong Nguyen, Laura Rosseel, and Jack Grieve. 2021. https://doi.org/10.18653/v1/2021.naacl-main.50 On learning and representing social meaning in NLP : a sociolinguistic perspective . In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 603--612, Online. Ass...

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" 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 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Ciosici and Ira Assent

Manuel R. Ciosici and Ira Assent. 2018. https://aclanthology.org/C18-2001 Abbreviation expander - a web-based system for easy reading of technical documents . In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 1--4, Santa Fe, New Mexico. Association for Computational Linguistics

work page 2018

[4] [4]

Dinh-Truong Do, Ha Thanh Nguyen, Thang Ngoc Bui, and Hieu Dinh Vo. 2021. Vsec: Transformer-based model for vietnamese spelling correction. In PRICAI 2021: Trends in Artificial Intelligence, pages 259--272, Cham. Springer International Publishing

work page 2021

[5] [5]

Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng, and Ahmed Hassan Awadallah. 2021. https://doi.org/10.18653/v1/2021.naacl-main.66 Self-training with weak supervision . In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 845--863, Online. Associa...

work page doi:10.18653/v1/2021.naacl-main.66 2021

[6] [6]

Benjamin Muller, Benoit Sagot, and Djam \'e Seddah. 2019. https://doi.org/10.18653/v1/D19-5539 Enhancing BERT for lexical normalization . In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 297--306, Hong Kong, China. Association for Computational Linguistics

work page doi:10.18653/v1/d19-5539 2019

[7] [7]

Anh Thi-Hoang Nguyen, Dung Ha Nguyen, Nguyet Thi Nguyen, Khanh Thanh-Duy Ho, and Kiet Van Nguyen. 2024 a . Automatic textual normalization for hate speech detection. In Intelligent Systems Design and Applications, pages 1--12, Cham. Springer Nature Switzerland

work page 2024

[8] [8]

Dong Nguyen, Laura Rosseel, and Jack Grieve. 2021. https://doi.org/10.18653/v1/2021.naacl-main.50 On learning and representing social meaning in NLP : a sociolinguistic perspective . In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 603--612, Online. Ass...

work page doi:10.18653/v1/2021.naacl-main.50 2021