arxiv: 2604.18034 · v1 · submitted 2026-04-20 · 💻 cs.CL · cs.CV

Recognition: unknown

SignDPO: Multi-level Direct Preference Optimisation for Skeleton-based Gloss-free Sign Language Translation

Muxin Pu , Xiao-Ming Wu , Mei Kuan Lim , Chun Yong Chong , Wei Li , Chen Change Loy

Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:13 UTC · model grok-4.3

classification 💻 cs.CL cs.CV

keywords sign language translationdirect preference optimizationskeleton-based modelsgloss-free translationpreference alignmentsemantic drifthierarchical perturbationself-guiding attention

0 comments

The pith

SignDPO shifts skeleton-based sign language translation from imitation learning to multi-level preference alignment across spatial, temporal, and linguistic dimensions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Skeleton-based sign language models trained with maximum likelihood estimation often produce translations that drift from the intended meaning because they copy sequences without learning to reject fine-grained errors in movement or timing. SignDPO replaces this with direct preference optimization that creates and ranks better versus worse versions of the same input at three levels. It automatically perturbs skeletal poses globally and locally, uses decoder attention to target semantically important regions, and fine-tunes a separate model to generate language-level preference pairs. If the central claim holds, gloss-free systems can close the gap with gloss-based ones by learning what makes a translation correct rather than merely likely. Experiments on CSL-Daily, How2Sign, and OpenASL show consistent gains over prior gloss-free methods.

Core claim

The paper establishes that a multi-level DPO framework, built from hierarchical spatial-temporal perturbations, decoder cross-attention guidance for salient region selection, and an automated language-level preference generator, allows skeleton-based models to optimize for semantic alignment rather than sequence mimicry and thereby reduce semantic drift in gloss-free sign language translation.

What carries the argument

The multi-level direct preference optimisation framework that constructs non-preferred samples through hierarchical perturbations and self-guiding attention to enforce distinctions between correct and semantically drifted outputs.

If this is right

Models learn to reject structural distortions in skeletal trajectories that would otherwise produce incorrect word sequences.
Preference pairs can be generated without human annotation at any of the three levels.
The resulting systems surpass existing gloss-free methods on CSL-Daily, How2Sign, and OpenASL.
Performance approaches that of established gloss-based pipelines on the same data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The attention-guided perturbation step may identify which body parts carry the heaviest semantic load for particular signs.
The same hierarchical preference construction could be applied to other high-entropy sequence tasks such as motion capture to text.
Combining the language-level preference model with larger pretrained text generators might further reduce output-level failures.

Load-bearing premise

The perturbations at spatial, temporal, and language levels generate non-preferred samples that reflect genuine semantic drift rather than mere superficial changes.

What would settle it

Retraining the same base model on one of the three benchmarks using only the new preference pairs and observing no gain or a drop in standard translation metrics such as BLEU or ROUGE relative to the original MLE baseline.

Figures

Figures reproduced from arXiv: 2604.18034 by Chen Change Loy, Chun Yong Chong, Mei Kuan Lim, Muxin Pu, Wei Li, Xiao-Ming Wu.

**Figure 2.** Figure 2: The overview illustrates the difference between (a) [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of the SignDPO Framework. (a) Multi-level perturbation: Non-preferred (negative) samples are constructed [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Pipeline of the Language Perturbation Model. Imperfect translations are first generated by the pre-trained SLT model [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Hyperparameter sensitivity on the CSL-Daily test [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

read the original abstract

We present SignDPO, a novel multi-level Direct Preference Optimisation (DPO) framework designed to enhance the alignment of skeleton-based Sign Language Translation. While current skeleton-based models have made significant progress using Maximum Likelihood Estimation, they are primarily constrained by an imitation-based paradigm that lacks discriminative sensitivity to the fine-grained spatio-temporal nuances of sign language, often leading to semantic drift. To address this, SignDPO shifts the optimisation goal from simple sequence mimicry to structured preference alignment across spatial, temporal, and linguistic dimensions. Our framework involves three key designs. First, we introduce a hierarchical perturbation strategy to construct spatial and temporal non-preferred samples at both global and local granularities automatically. Second, we propose a self-guiding mechanism that leverages decoder cross-attention scores to identify and perturb semantically salient skeletal regions, forcing the model to distinguish genuine sign signals from structural distortions. Third, we establish an automated language-level preference generator by fine-tuning a dedicated perturbation model, capturing complex output-level failure modes without manual annotation. Extensive experiments on three widely adopted benchmarks, CSL-Daily, How2Sign, and OpenASL, demonstrate that SignDPO consistently outperforms state-of-the-art gloss-free methods and even rivals established gloss-based ones. Our results suggest that multi-level preference alignment is a powerful paradigm for bridging the gap between high-entropy skeletal trajectories and discrete linguistic semantics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SignDPO extends DPO to skeleton-based sign language translation with hierarchical perturbations and attention-guided pairs, but the evidence does not yet confirm those pairs capture genuine semantic differences rather than kinematic noise.

read the letter

SignDPO takes the DPO objective and applies it to gloss-free sign language translation from skeletons. It builds preference pairs at three levels: hierarchical spatial and temporal perturbations for non-preferred samples, decoder cross-attention to select and distort salient regions, and a separate fine-tuned model that generates language-level alternatives. The shift from pure imitation to structured preference alignment across those dimensions is the concrete addition. On CSL-Daily, How2Sign, and OpenASL the method beats prior gloss-free baselines and comes close to gloss-based systems, which matters because gloss labels are costly to collect. The designs are specific enough that someone could reimplement the perturbation pipeline and the automated generator without starting from scratch. That counts as useful engineering progress in this subfield. The central assumption still needs checking. The non-preferred samples are supposed to reflect semantic drift, yet the abstract gives no human ratings on the pairs, no correlation between perturbation strength and translation error, and no ablation that isolates whether the gains come from meaning contrast or from simply adding more varied training signals. If the perturbations mainly scramble trajectories without changing what the sign conveys, the DPO step reduces to regularized sequence modeling rather than true preference learning. The stress-test note is on target given the information provided. The paper does not introduce new equations or formal derivations, which is acceptable for an application, and the citations track the relevant DPO and sign-language literature. This work is aimed at people already working on sign language translation or on adapting preference optimization to continuous motion inputs. A reader who wants a ready-to-try recipe for multi-level alignment on skeleton sequences will get value from the three benchmark results and the described components. It deserves peer review because the idea is well-scoped, the benchmarks are standard, and the performance claims are testable even if the current write-up leaves the semantic validity of the pairs open to question. Referees can ask for the missing validation without the paper being rejected outright.

Referee Report

2 major / 2 minor

Summary. The manuscript presents SignDPO, a multi-level Direct Preference Optimization framework for skeleton-based gloss-free sign language translation. It replaces standard MLE training with structured preference alignment by automatically constructing non-preferred samples via hierarchical spatial/temporal perturbations at global and local levels, a self-guiding mechanism that uses decoder cross-attention scores to target semantically salient skeletal regions, and a fine-tuned language-level perturbation model to generate output-level failure modes. Experiments on CSL-Daily, How2Sign, and OpenASL are reported to show consistent gains over gloss-free baselines and competitiveness with gloss-based methods.

Significance. If the preference pairs are shown to encode genuine semantic differences, the work offers a practical route to move sign language translation beyond imitation-based objectives toward discriminative alignment across spatial, temporal, and linguistic levels. The fully automated construction of preference data without manual annotation is a clear engineering strength and could transfer to other continuous-to-discrete sequence tasks.

major comments (2)

[§3.2] §3.2 (hierarchical perturbation and self-guiding mechanism): The central claim that these perturbations produce non-preferred samples reflecting semantic drift rather than superficial kinematic noise is load-bearing for the entire DPO objective. The manuscript supplies no human semantic ratings, no correlation between perturbation severity and downstream BLEU/ROUGE degradation, and no ablation comparing attention-guided perturbations against random or uniform ones on semantic metrics. Without such validation, the multi-level alignment may reduce to regularized MLE on trajectory statistics.
[§4.1–4.3] §4.1–4.3 (experimental tables): The reported outperformance is presented without error bars, without the number of random seeds, and without an ablation isolating the contribution of each perturbation level (spatial vs. temporal vs. language-level). This makes it impossible to assess whether the gains are robust or driven by a single component.

minor comments (2)

[§3.3] §3.3: The fine-tuning procedure for the language-level perturbation model (base architecture, training corpus, and hyper-parameters) is described at too high a level for reproducibility.
[Figure 2 and §3.1] Figure 2 and §3.1: The notation for global vs. local perturbation operators is introduced without an explicit equation or pseudocode, making the hierarchical construction difficult to follow precisely.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive assessment of SignDPO and for the detailed comments, which help clarify how to strengthen the validation of our preference construction pipeline. We respond to each major comment below and will incorporate the suggested analyses in the revised manuscript.

read point-by-point responses

Referee: [§3.2] §3.2 (hierarchical perturbation and self-guiding mechanism): The central claim that these perturbations produce non-preferred samples reflecting semantic drift rather than superficial kinematic noise is load-bearing for the entire DPO objective. The manuscript supplies no human semantic ratings, no correlation between perturbation severity and downstream BLEU/ROUGE degradation, and no ablation comparing attention-guided perturbations against random or uniform ones on semantic metrics. Without such validation, the multi-level alignment may reduce to regularized MLE on trajectory statistics.

Authors: We agree that direct evidence linking perturbations to semantic rather than purely kinematic changes is important. The self-guiding mechanism is explicitly motivated by the observation that decoder cross-attention concentrates on linguistically meaningful skeletal joints and frames; perturbing those regions is intended to create preference pairs that penalize semantic drift. The consistent gains over strong gloss-free baselines across three datasets provide indirect support that the DPO objective is not merely regularized MLE. Nevertheless, we acknowledge the absence of the requested validations. In revision we will add (i) an ablation of attention-guided versus random/uniform perturbations evaluated on BLEU/ROUGE, (ii) plots correlating perturbation severity with metric degradation, and (iii) a limitations paragraph noting that large-scale human semantic ratings were outside the current resource scope. These additions will make the semantic grounding explicit without altering the core technical contribution. revision: partial
Referee: [§4.1–4.3] §4.1–4.3 (experimental tables): The reported outperformance is presented without error bars, without the number of random seeds, and without an ablation isolating the contribution of each perturbation level (spatial vs. temporal vs. language-level). This makes it impossible to assess whether the gains are robust or driven by a single component.

Authors: We concur that reporting variance, seed counts, and component-wise ablations is necessary for assessing robustness. The experiments were run with multiple random seeds, yet the variance and exact seed count were omitted from the tables. In the revised manuscript we will (i) add error bars computed over three independent seeds for all main results, (ii) state the seed count explicitly in §4, and (iii) include a new ablation table that isolates the contribution of the spatial, temporal, and language-level perturbation modules. This will demonstrate that the observed improvements arise from the combination of all three levels rather than any single component. revision: yes

Circularity Check

0 steps flagged

No circularity: SignDPO applies standard DPO to externally generated preference pairs

full rationale

The paper extends the existing DPO objective (from prior non-self literature) to skeleton-based sign language translation by constructing preference pairs via hierarchical spatial/temporal perturbations and attention-guided self-perturbation. No equations are presented that define a success metric or prediction in terms of the method's own fitted parameters; the claimed gains are measured on independent external benchmarks (CSL-Daily, How2Sign, OpenASL) rather than reducing to the perturbation process by construction. No self-citations are load-bearing for uniqueness theorems, ansatzes, or core derivations, and the framework does not rename known results or smuggle assumptions via author-overlapping citations. The derivation chain remains self-contained with external empirical validation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no explicit free parameters, axioms, or invented entities beyond the high-level description of the framework; all technical details remain unavailable.

pith-pipeline@v0.9.0 · 5563 in / 1178 out tokens · 44318 ms · 2026-05-10T04:13:35.801709+00:00 · methodology

discussion (0)

Reference graph

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