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REVIEW 2 major objections 6 minor 29 references

JNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models

T0 review · 2 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Instruction-tuning a 32-billion-parameter generative model with a pairwise, one-emotion-at-a-time prompt and training on all languages together places it in the top four for ten languages in SemEval-2025 Task 11 emotion detection, first…

desk verdict A credible SemEval-2025 system description whose leaderboard results stand, but the printed prompts omit one of the six emotion labels, so the paper needs a correction before it can serve as a reproducible recipe. read the letter →

arxiv 2505.13244 v1 pith:3TXGF5UN submitted 2025-05-19 cs.CL cs.LG

Jieying Xue , Phuong Minh Nguyen , Minh Le Nguyen , Xin Liu This is my paper · ORCID
classification cs.CLcs.LG
keywords multilingualemotiondetectionmulti-labelclassificationinstructionfine-tuninglargelanguagemodelspairwiseLoRAlow-resourcelanguagescross-lingualtransfer
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 that a simple recipe—taking a 32-billion-parameter generative model, instruction-tuning it with LoRA, and asking it to judge each emotion separately rather than produce the full label set at once—generalizes across languages in the SemEval-2025 Task 11 emotion-detection shared task. The evidence offered is competitive standing: top-four macro-F1 in ten Track A languages, first in Hindi, and top-five Pearson correlation in seven Track B languages. The authors further report that training one model on all languages mixed together beats per-language models, and that the pairwise 'one emotion at a time' question beats a base prompt that asks for all labels when sentences carry multiple emotions, while the base prompt wins in sparse-label languages. The recipe also transfers from label detection (Track A) to intensity estimation (Track B) by changing only the expected answer format. If the recipe is right, multilingual multi-label emotion detection does not need language-specific engineering; the question decomposition and the training mixture do the work.

What carries the argument

The load-bearing object is the pairwise instruction template: a system prompt that names the emotion label set, a user turn of the form 'Given the sentence: ..., is the emotion anger expressed in it?' (Track A) or 'what is the intensity of the emotion fear?' (Track B), and an assistant answer of 'yes'/'no' or 'low'/'moderate'/'high'. LoRA fine-tuning of Qwen2.5-32B under a causal language-modeling objective on all languages concatenated is the training mechanism, and the base template—asking for all labels at once—is the control that isolates the benefit of per-emotion decomposition.

What would settle it

Run the exact Table 5 and Table 6 prompts—with the five-emotion label set printed in the paper—through a LoRA-fine-tuned Qwen2.5-32B on the BRIGHTER development set and compare macro-F1 and Pearson values with the development columns of Tables 3 and 4. A second check: if the model ever emits 'disgust' despite the prompt not listing it, the printed template cannot be the deployed prompt, and the reported numbers are not reproducible from the paper alone.

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Extended reading notes

Core claim

The central discovery is that multi-label emotion detection in many languages can be reformulated as a set of per-emotion generation questions and solved by a single instruction-tuned LLM: for Track A, ask 'is emotion ye expressed?' and read 'yes' or 'no'; for Track B, ask for the intensity of ye and read 'low', 'moderate', or 'high'. With Qwen2.5-32B fine-tuned under LoRA on all training languages jointly, this pairwise strategy achieves the paper's reported rankings, while a base strategy that maps a sentence directly to all labels is competitive only where most sentences have zero or one emotion. Mixed-language training consistently improves over separate per-language models, and the generative model generally outperforms a fine-tuned XLM-RoBERTa encoder. The authors thus claim the pairwise decomposition, not model size or language-specific tuning, is the ingredient that carries the generalization.

Load-bearing premise

The reported rankings are only reproducible if the printed instruction templates are the exact prompts that produced the submitted predictions, and the paper's own tables show a mismatch—the printed label set has five emotions while outputs and evaluation include 'disgust' as a sixth.

Editorial extensions

If this is right

  • A single mixed-language instruction-tuned model can replace per-language pipelines for multilingual emotion detection, which matters most for low-resource languages where separate training data are thin.
  • Teams facing multi-label emotion data should default to the pairwise question format when sentences regularly carry several emotions, and to the base format when labels are sparse.
  • The same pairwise prompt structure covers both label detection and intensity prediction, so one fine-tuned model can serve both tasks without architectural changes.
  • Instruction-tuned generative models of the Qwen2.5-32B class are a viable alternative to fine-tuned encoders for this task, with the main trade-off being higher inference cost.

Reading between the lines

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

  • A likely reason mixed-language training helps is that it balances the emotion-label distribution across languages; a testable corollary is that per-language models should catch up when trained on resampled or augmented data that equalizes label frequencies.
  • The five-emotion prompt printed in Table 1 cannot explain the 'disgust' rows in Figure 3 and Table 2, so the deployed prompt almost certainly listed six emotions; if so, the paper's template is illustrative rather than the exact submission prompt.
  • The authors' suggested logit-aggregation variant is an immediate cheap improvement: instead of decoding 'yes' or 'no', sum the logits of both tokens and threshold the softmax probability, which should yield calibrated per-language thresholds.
  • The pairwise recipe should transfer to other multi-label text-generation problems, such as aspect-based sentiment or emotion-in-conversation, because the conditioning mechanism (one label at a time) is independent of the label inventory.
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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

2 major / 6 minor

Summary. The paper describes a system for SemEval-2025 Task 11, covering Track A (multi-label emotion detection across 28 languages in the BRIGHTER dataset) and Track B (emotion intensity prediction across 11 languages). Two architectures are compared: a fine-tuned BERT-based classifier and instruction-tuned generative LLMs (Llama-2 and Qwen-2.5), under two multi-label strategies: a base strategy that generates all emotion labels at once, and a pairwise strategy that considers each emotion separately. The authors also compare per-language training against mixed-language training. The reported results are external leaderboard scores: top-four rankings in 10 Track A languages including first place in Hindi, and top-five rankings in 7 Track B languages. The paper additionally analyzes when pairwise versus base strategies help, the effect of label imbalance, and the role of mixed-language training.

Significance. If the reported leaderboard scores are taken at face value, the paper provides a credible and simple competitive recipe for multilingual multi-label emotion detection: instruction-tuned Qwen-32B with pairwise prompting performed strongly across many languages, and the code is publicly available. The central empirical claim rests on an external benchmark with no fitted constants, so circularity is not a concern. The main weaknesses are internal-consistency problems in the system description and two overgeneralized auxiliary findings; these are fixable and do not undermine the external leaderboard evidence.

major comments (2)
  1. [Table 1; Appendix A (§3.2)] The instruction templates printed in Table 1 and in Appendix A (Tables 5 and 6) define the emotion label set as {anger, fear, joy, sadness, surprise}, omitting 'disgust'. This contradicts Table 2, whose Track A output example is 'disgust, sadness'; Figure 3, which reports performance for 'disgust'; and Section 5.3, which discusses 'disgust' as a class. Since the official BRIGHTER label set includes disgust, the reader cannot tell from the text whether the submitted runs used the printed prompt (in which case the model would have to ignore the system instruction to produce disgust) or a different prompt that included disgust (in which case the paper does not describe the actual submitted system). Please correct the label set in all templates and state explicitly the exact prompts used for the submitted Track A and Track B runs, including how 'disgust' was handled.
  2. [§5.3, Tables 3–4] The claim that 'mixed-language training... demonstrates superior performance compared to training separate models' is not supported for Track A by the data in Table 3. The only mixed-language Track A row reported (Qwen-32b base mixed langs) has average 0.5933, identical to the Qwen-32b pairwise separated langs average of 0.5933, and no pairwise-mixed Track A result is shown; in contrast, Table 4 does show pairwise mixed (0.7363) beating pairwise separated (0.7234) in Track B. Please either report the pairwise-mixed Track A result or qualify this claim to Track B only.
minor comments (6)
  1. [§5.1, Table 3] The statement that 'in low-resource languages, LLMs performed poorly, whereas the RoBERTa-based approach yielded better results' is contradicted by several rows in Table 3: for afr, arq, ibo, kin, pcm, vmw, and yor, the Qwen-32B models beat xml-roberta by a clear margin. Please qualify the claim to specific languages or report a systematic comparison.
  2. [§3.2, Eq. (4)] The BERT-based multi-label classifier is described as using a sigmoid output layer, but the loss is said to be cross-entropy. For multi-label classification with independent sigmoid outputs, the appropriate objective is binary cross-entropy per label; please correct this.
  3. [Table 3] The column header 'orm(development)' appears to be a typo for 'orm'. Please also clarify in the caption whether the reported development results are on the official development set or on the 10% internal held-out set described in Section 4.
  4. [§3.1, Eqs. (1)–(2)] The notation in equations (1) and (2) is ambiguous (for example, P_A({0,1} | x, y_e) mixes set notation with probability notation). Please define all symbols and describe the decoding procedure that maps generated text to predicted labels and intensity values.
  5. [Abstract, §5.1] The phrase 'Top 4 performance across 10 languages' is ambiguous because Section 5.1 reports a mix of 1st, 2nd, 3rd, and 4th place rankings. Please rephrase as 'ranked among the top four in 10 languages' for clarity.
  6. [§2] There is a grammatical error in the sentence 'their effectiveness in multilingual sentiment analysis is often limited without additional fine-tuning is performed'; it should read '...without additional fine-tuning being performed'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: reported results are leaderboard scores on the official SemEval-2025 Task 11 test set, produced by standard fine-tuning; the derivation contains no fitted constants, no prediction that is a fitted input in disguise, and no load-bearing self-citation.

full rationale

The paper's central claims are empirical leaderboard rankings (Track A top 4 in 10 languages, 1st in Hindi; Track B top 5 in 7 languages) on the external SemEval-2025 Task 11 benchmark. The method (Section 3) is described as standard BERT fine-tuning and instruction-tuned LLM generation with LoRA, trained on the official BRIGHTER training data and evaluated on the held-out development and test sets. There is no equation that defines a quantity in terms of the target output, and no parameter fitted to the test set that is later renamed as a prediction. The pairwise/base strategies are presented as modeling choices and are compared empirically rather than derived from the results they supposedly predict. The only self-citation, Xue et al. (2024), is invoked as general inspiration ('building upon recent SOTA methods in the field of emotion recognition') and is not used to justify the reported rankings or to exclude alternative methods; the actual training recipe is standard instruction fine-tuning (Chung et al., 2022; Hu et al., 2022). Therefore the self-citation is not load-bearing. One descriptive inconsistency exists: Table 1's prompt template restricts the emotion set to {anger, fear, joy, sadness, surprise}, while Table 2 and Figure 3 include 'disgust', and the official task label set has six emotions. This is a reproducibility/correctness concern about whether the printed prompts match the deployed system, not a circularity of derivation; it does not make any claim equivalent to its input. Under the hard rules, no specific reduction can be exhibited, so the circularity score is 0.

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

The central results are empirical leaderboard scores with no new derivation. The uncharged assumptions are that the printed prompt matches the deployed prompt (which appears false with respect to "disgust"), that output parsing is straightforward, and that the official test numbers are accurate. No new theoretical entities are introduced.

free parameters (3)
  • learning rate = 3e-4
    Reported as a fixed hyperparameter for all runs; chosen by hand, not fitted to the test set.
  • training epochs = 5-6
    Reported as a range, and the exact epoch is selected using the internal development set, adding a selection choice.
  • LoRA configuration = not reported
    LoRA is used for efficient fine-tuning, but rank, alpha, and target modules are not specified, so the trainable capacity is underdetermined.
assumptions (3)
  • domain assumption The official BRIGHTER and SemEval test labels are the ground truth, and the reported test scores are official results rather than recomputed values.
    All leaderboard rank claims in Section 5 depend on this; the post-evaluation asterisk for German shows that some reported numbers come from a later phase.
  • domain assumption Generated label text from the LLM was mapped unambiguously to the predefined label set without unstated decoding rules.
    The paper never describes how outputs like "yes/no" or "moderate degree of anger" are parsed into labels, yet the per-sample F1 analysis in Section 5.3 requires such a mapping.
  • domain assumption The printed instruction prompt is the prompt actually used for the submitted predictions.
    Table 1 omits "disgust" from the label set even though Table 2 and Figure 3 use it, so this assumption is violated on its face and the real prompt is not fully documented.

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Cite this review

Pith. "Pith review of JNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models." pith.science (2026). https://pith.science/paper/3TXGF5UN

@misc{pith2026250513244,
  author       = {Pith},
  title        = {Pith review of: JNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3TXGF5UN}},
  note         = {Machine review of arXiv:2505.13244}
}
read the original abstract

With the rapid advancement of global digitalization, users from different countries increasingly rely on social media for information exchange. In this context, multilingual multi-label emotion detection has emerged as a critical research area. This study addresses SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. Our paper focuses on two sub-tracks of this task: (1) Track A: Multi-label emotion detection, and (2) Track B: Emotion intensity. To tackle multilingual challenges, we leverage pre-trained multilingual models and focus on two architectures: (1) a fine-tuned BERT-based classification model and (2) an instruction-tuned generative LLM. Additionally, we propose two methods for handling multi-label classification: the base method, which maps an input directly to all its corresponding emotion labels, and the pairwise method, which models the relationship between the input text and each emotion category individually. Experimental results demonstrate the strong generalization ability of our approach in multilingual emotion recognition. In Track A, our method achieved Top 4 performance across 10 languages, ranking 1st in Hindi. In Track B, our approach also secured Top 5 performance in 7 languages, highlighting its simplicity and effectiveness\footnote{Our code is available at https://github.com/yingjie7/mlingual_multilabel_emo_detection.

Figures

Figures reproduced from arXiv: 2505.13244 by the authors.

Figure 1
Figure 1. Distribution of improved samples between [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Distribution of improved samples between [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

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