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REVIEW 4 major objections 5 minor 32 references

An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamese

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A simplified HPSG neural parser, adapted to Vietnamese with PhoBERT, is claimed to reach 82.34% constituency F1 on VietTreebank and 89.04% on the VLSP 2023 private test, with lower LAS attributed to label-preserving random permutations…

desk verdict Honest transfer attempt whose headline SOTA numbers are confounded by unconstrained random permutation of training data; fixable with an ablation and data release. read the letter →

arxiv 2411.17270 v2 pith:5KMRARF2 submitted 2024-11-26 cs.CL

Duc-Vu Nguyen , Thang Chau Phan , Quoc-Nam Nguyen , Kiet Van Nguyen , Ngan Luu-Thuy Nguyen This is my paper · ORCID
classification cs.CL
keywords VietnameseparsingHPSGconstituencydependencyPhoBERTVietTreebankVnDTVLSP2023
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

This paper tries to establish that a simplified, joint-span Head-Driven Phrase Structure Grammar parser, originally built for English, can be made to work for Vietnamese by replacing its encoder with PhoBERT or XLM-RoBERTa and by repairing the roughly 15% of VietTreebank and VnDT tree pairs that violate the grammar's constraints. The repair is done by randomly permuting the offending training and development trees, keeping the original dependency labels untouched because no linguistic expert was consulted. The experiments report an 82.34% constituency F-score on the VietTreebank test set, an 89.04% F-score on the VLSP 2023 private test (above the Stanza baseline's 88.73%), and a UAS of 85.73 on VnDT, with lower LAS attributed to the label-preserving permutation. The paper concludes that simplified HPSG should receive more linguistic-expert attention when Vietnamese treebanks are built.

What carries the argument

The load-bearing object is the joint-span simplified HPSG tree: a constituent-style tree in which every span carries both a syntactic category and a head word, so dependency arcs are embedded in the same span structure. The parser scores spans and head-arcs with a biaffine attention model over self-attentive token representations built from PhoBERT or XLM-RoBERTa, character, word, and POS embeddings, and decodes with dynamic programming. The paper's adaptation also adds a pre-processing repair: roughly one thousand non-compliant constituency/dependency tree pairs in the training and development splits are randomly permuted so that every pair satisfies the simplified HPSG constraints. That repair, plus the pretrained Vietnamese encoders, is what carries the claimed transfer from Penn Treebank to Vietnamese.

What would settle it

Retrain the same parser using linguist-corrected versions of the roughly one thousand tree pairs that were randomly permuted: if LAS does not rise above the reported 78.42 or F1 drops below 82.34, the paper's account of the repair's effect is falsified.

Watch

Extended reading notes

Core claim

The paper claims that a joint-span simplified HPSG neural parser, originally built for the Penn Treebank, can be transferred to Vietnamese by swapping in PhoBERT or XLM-RoBERTa encoders and by repairing the ~15% of VietTreebank/VnDT tree pairs that violate simplified HPSG rules through random permutation of training and development items. In the authors' experiments this adaptation reaches an 82.34% constituency F-score on the VietTreebank test set with predicted POS tags, above the self-attentive PhoBERT-large baseline's 80.55%, and an 89.04% F-score on the VLSP 2023 private test, slightly above Stanza's 88.73%. On dependency parsing it reports a UAS of 85.73 on VnDT, higher than the PhoNLP baseline, while LAS stays lower, which the authors attribute to keeping original dependency labels untouched during permutation. The paper's conclusion is that simplified HPSG deserves more linguistic-expert attention when building Vietnamese treebanks.

Load-bearing premise

The load-bearing premise is that randomly permuting the roughly one thousand non-compliant tree pairs in the training and development sets, without linguistic review, produces valid training trees rather than noise that quietly trains the parser to accept ungrammatical structures.

Editorial extensions

If this is right

  • With PhoBERT-large and predicted POS tags, the joint-span HPSG parser reaches 82.34% constituency F1 on the VietTreebank test set, above the self-attentive PhoBERT-large baseline's 80.55%.
  • On the VLSP 2023 private test the same model scores 89.04% F1, beating the Stanza baseline's 88.73%, while the two are nearly tied on the public test (86.05% vs 85.87%).
  • On VnDT dependency parsing the parser reports UAS 85.73 with PhoBERT-large, above the PhoNLP baseline's 84.95, but lower LAS, which the authors attribute to keeping original dependency labels while randomly permuting arcs.
  • Tuning the constituency/dependency loss weight to 0.9 on the development set indicates that the random-permutation repair did not disrupt the balance between the two parsing tasks.

Reading between the lines

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

  • If the random-permutation repair is as benign as the development curves suggest, the same preprocessing could be tried on other languages whose treebanks pair constituency and dependency annotations, with the 15% violation rate as a natural diagnostic.
  • The paper's LAS result points to a direct follow-up: re-run the same architecture on the permuted spans but with linguist-corrected head and dependency labels; if LAS rises without hurting UAS, the label-preserving choice is the cause.
  • Because the VLSP 2023 head rules were written by non-linguists, an expert-written version of those rules is a testable way to see whether the HPSG parser's margin over Stanza widens further on out-of-domain data.
  • A more skeptical probe would compare per-tree error patterns on the original versus permuted training trees; if the parser performs worse on sentences whose trees were permuted, the repair is introducing a systematic bias rather than a neutral fix.
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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 adapts the joint-span HPSG parser of Zhou and Zhao (ACL 2019) to Vietnamese by replacing its word encoders with PhoBERT and XLM-RoBERTa and by modifying the training data. Because roughly 15% of the constituent/dependency tree pairs in VietTreebank (VTB) and VnDT do not satisfy the simplified HPSG constraints, the authors randomly permute the non-compliant pairs in the training and development sets without linguistic constraints. They report a constituency F1 of 82.34 on the VTB test set, a dependency UAS of 85.73 on VnDT, and an F1 of 89.04 on the VLSP 2023 private test, claiming state-of-the-art results. For the VLSP 2023 treebank, which has no dependency annotation, the authors design head-percolation rules and use ClearNLP to convert constituency trees into dependency trees. The paper's central claim is that this simplified-HPSG parser outperforms prior Vietnamese parsers when trained on the modified corpora.

Significance. If the reported results were obtained under controlled conditions, the paper would provide a useful data point that the Zhou-Zhao joint-span HPSG architecture transfers to Vietnamese when combined with PhoBERT-large, and it would support the authors' suggestion that treebank construction should pay more attention to simplified HPSG constraints. The paper has several strengths: the test sets are left unaltered, the models are run five times, baseline systems are replicated in some cases, and the authors are transparent about the non-linguistic nature of their data modifications and head rules. Code and toolkit links are provided. The weakness is that the headline comparisons are not controlled: the HPSG parser is trained on randomly permuted data while the baselines are trained on the original treebanks, and no ablation separates the effect of the data modification from the effect of the architecture. The VLSP 2023 claim also rests on an unvalidated constituency-to-dependency conversion. These issues are fixable with additional experiments, but they are load-bearing for the stated state-of-the-art conclusions.

major comments (4)
  1. [§5.2, Tables 1–2] The HPSG parser is trained on data in which roughly 1,000 non-compliant tree pairs were "randomly permuted ... not bound by linguistic constraints," while the Self-Attentive, Biaffine, and PhoNLP baselines were trained on the original treebanks. This changes the training distribution, so the reported F1 and UAS gains cannot currently be attributed to the simplified HPSG architecture rather than to the permutation itself. The λ sweep in Figure 3 only varies the loss weight and does not compare permuted versus unpermuted training data, so the statement in §5.2 that the permutation's impact was "minimal" is not supported. I request an ablation training the same HPSG parser on (a) the original, unpermuted data and (b) data with non-compliant pairs removed, reporting F1, UAS, and LAS for both conditions.
  2. [§5.2, Figure 4, Table 3] The VLSP 2023 result depends on head-percolation rules written "by individuals with a non-linguistic engineering background" and on a ClearNLP conversion, but no validation of the resulting dependency trees is reported. Because the HPSG parser requires dependency supervision and the 89.04 private-test F1 exceeds the Stanza parser by only 0.31 points, the quality of this conversion could be decisive for the claimed advantage. Please compare the converted dependencies against an existing rule set or gold-standard dependencies, or provide a manual error analysis of the converted trees.
  3. [Tables 1 and 2] The headline numbers are presented without variance, even though the models are run five times. Additionally, Table 2 marks a result as "significantly different" when the lowest of five HPSG runs is compared with the replicated PhoNLP score using an unspecified paired t-test; selecting the minimum run before testing is not a valid significance procedure. Please report mean ± standard deviation over all five seeds for every row, and use a proper paired test (e.g., bootstrap or matched-seed comparison) for claims of statistical significance.
  4. [§3.1 vs. §5.2] The paper is inconsistent about which data were permuted: §3.1 says "samples from the training and development sets were adjusted" for the VTB and VnDT corpora, while §5.2 says "our modifications were limited to the training and development subsets of the VnDT dataset." This distinction matters because it determines whether the VTB constituency F1 comparison is also confounded by the modified training data. Please clarify whether the constituency trees in the VTB training set were modified or left untouched.
minor comments (5)
  1. [§5.4, Figure 5(a)] The text says the system "scored zero on categories like Nby and ADJb," but Figure 5(a) shows Nby at 8.00% and ADJb at 38.79%; please correct the mismatch.
  2. [§3.1, reference [25]] The sentence "Nguyễn et al. [25] introduce our project on developing a Vietnamese lexicon" is confusing, because reference [25] is a lexicon paper and not the current project; please rephrase.
  3. [Footnote 6] The URL path contains a duplicated suffix "run_constituency .py .py"; please fix the typo.
  4. [Figure 4] The head-percolation rules are printed as one long, low-resolution rule string; please provide a readable table of the rules or move them to an appendix.
  5. [Table 1, caption and §5.2] The caption says predicted POS tags were generated with the VnCoreNLP toolkit, while §5.2 describes a fine-tuned Stanza tagger with PhoBERT-large; please clarify which tagger produced the POS tags used in Table 1 and Table 2.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported F1/UAS/LAS scores are empirical benchmarks on unmodified test sets, not results forced by construction or by self-citation.

full rationale

The paper contains no derivation chain in which a predicted quantity is defined in terms of the fitted quantity or vice versa. The closest step is Section 5.2, where roughly 1,000 VTB/VnDT training and development tree pairs are randomly permuted, 'not bound by linguistic constraints,' to satisfy the simplified HPSG criteria of Zhou and Zhao [13]. This is a training-data modification, and it may raise concerns about comparability with baselines trained on the original corpora, but it is not circular: the VnDT test set is explicitly left unaltered, and the VLSP 2023 private test is an external evaluation set. The reported constituency F1 of 82.34, UAS of 85.73, and VLSP F1 of 89.04 are computed against original gold annotations, so they are not identities or fitted values renamed as predictions. The tuning of the loss weight λ on development data (Section 5.2, Figure 3) is standard hyperparameter selection, not a fitted input called a prediction. Self-citations such as [29] are used as empirical baselines and are not load-bearing. No specific equation-level reduction or self-citation chain forces the paper's conclusions, so the appropriate finding is no significant circularity.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central claim rests on imported grammar rules, an unvalidated data-permutation procedure, and hand-written head rules for the VLSP treebank. The only numeric free parameter explicitly tuned is λ, though the random permutation itself introduces a non-numeric but substantial modeling choice.

free parameters (1)
  • loss weight lambda (λ) = 0.9
    Tuned on the VTB/VnDT development sets to balance constituency and dependency losses; Figure 3 shows performance varying with λ, and 0.9 was selected for all main experiments.
assumptions (3)
  • domain assumption Simplified HPSG tree constraints from [13] apply to Vietnamese syntax as a valid grammar formalism.
    The paper imports the simplified HPSG rule set from Penn Treebank [13] and uses it to judge VietTreebank/VnDT compliance without validating against Vietnamese linguistic theory (Section 3.1).
  • ad hoc to paper Randomly permuting non-compliant tree pairs yields valid training data.
    Section 5.2 states permutations were 'not bound by linguistic constraints' and labels preserved; no validation that resulting trees are grammatical or correct.
  • ad hoc to paper Head-percolation rules in Figure 4 adequately convert VLSP 2023 constituency trees to dependency trees.
    Rules were developed by non-linguists 'without direct linguistic input' (Section 5.2), yet used to train/evaluate dependency component.

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Cite this review

Pith. "Pith review of An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamese." pith.science (2026). https://pith.science/paper/5KMRARF2

@misc{pith2026241117270,
  author       = {Pith},
  title        = {Pith review of: An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamese},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5KMRARF2}},
  note         = {Machine review of arXiv:2411.17270}
}
read the original abstract

In this paper, we aimed to develop a neural parser for Vietnamese based on simplified Head-Driven Phrase Structure Grammar (HPSG). The existing corpora, VietTreebank and VnDT, had around 15% of constituency and dependency tree pairs that did not adhere to simplified HPSG rules. To attempt to address the issue of the corpora not adhering to simplified HPSG rules, we randomly permuted samples from the training and development sets to make them compliant with simplified HPSG. We then modified the first simplified HPSG Neural Parser for the Penn Treebank by replacing it with the PhoBERT or XLM-RoBERTa models, which can encode Vietnamese texts. We conducted experiments on our modified VietTreebank and VnDT corpora. Our extensive experiments showed that the simplified HPSG Neural Parser achieved a new state-of-the-art F-score of 82% for constituency parsing when using the same predicted part-of-speech (POS) tags as the self-attentive constituency parser. Additionally, it outperformed previous studies in dependency parsing with a higher Unlabeled Attachment Score (UAS). However, our parser obtained lower Labeled Attachment Score (LAS) scores likely due to our focus on arc permutation without changing the original labels, as we did not consult with a linguistic expert. Lastly, the research findings of this paper suggest that simplified HPSG should be given more attention to linguistic expert when developing treebanks for Vietnamese natural language processing.

Figures

Figures reproduced from arXiv: 2411.17270 by the authors.

Figure 1
Figure 1. Constituent, dependency, and joint span structures, [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Distributions in the VLSP 2023 Vietnamese Treebank training set. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Balancing Constituency and Dependency in Joint Span HPSG Parsing on the [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: An attempted version of head rules for the VLSP 2023 Vietnamese Treebank [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Our tagging and parsing results on the public set of the VLSP 2023 Vietnamese [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

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