{"id":"74065896-f9eb-411f-9a16-df91ea07d3dc","arxiv_id":"2507.01532","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"Pose normalization based on the signer's signing space substantially improves gloss-free sign language translation with a T5 model, while interpolation and augmentation give smaller, less certain gains.","lead":"This paper tests how different ways of cleaning up pose data, such as normalizing the signer's position and filling in missing hand or face keypoints, affect a sign language translation model's accuracy. It finds that these preprocessing choices change translation quality by a noticeable margin, and its analysis of where the model looks hints that adding extra register tokens could help.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The paper's central ablation rankings rest on an unvalidated assumption: 200k-iteration models are 'not fully trained' yet are claimed to reflect the rankings of fully trained models. Until convergence or ranking stability is shown, the quantitative conclusions are not reliable.","rationale":"The reader's weakest_assumption identifies the same load-bearing issue: the reliability of the 200k-iteration ablation rankings. This is the most fundamental threat to the paper's central claim because it affects every quantitative table and is explicitly acknowledged by the authors as an unvalidated belief. I considered whether test-set-based augmentation selection is more load-bearing; it is a real methodological flaw, but it is confined to the augmentation protocol and could be repaired by a validation split. The non-convergence assumption, by contrast, undermines the entire comparison framework. A concrete test can settle it, so a conditional acceptance (the reader's verdict) is appropriate. I agree with the reader's call to report variance, use a proper validation split, and provide convergence evidence. The paper's code release and clear writing are supportive, but they do not address the core evidential gap. Therefore no change to the reader's verdict is needed.","tokens_in":13735,"tokens_out":5137,"duration_ms":54830,"concrete_test":"Retrain the four normalization configurations (especially SignSpace and yaslf) for 400,000 iterations under the same schedule, evaluating on a held-out validation split (not the How2Sign test set) at checkpoints every 25,000 steps and across 5 seeds. Report mean and standard deviation of BLEU-4 at 200k and 400k. If the ranking between SignSpace and the next-best method persists at every checkpoint after 200k and the gap does not shrink by more than 30%, the concern is resolved; otherwise the ablation conclusions are not robust to training length.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim of significant gains from normalization, interpolation, and augmentation depends entirely on the relative BLEU scores in Tables 1-4. Section 4.1 states: 'the trained models after 200,000 iterations are not fully trained' and 'we believe the comparative performance after this shorter training protocol reflects the performance comparison of fully-trained models.' This is an explicit, load-bearing assumption with no supporting evidence. Since the learning rate is constant at 0.0004 after a 5,000-step warm-up, different preprocessing schemes can plausibly converge at different rates; a configuration that appears worse at 200k could overtake another at convergence. The paper also reports only the best of three seeds, so the point estimates carry unknown variance. For settings where the reported differences are small (e.g., interpolation BLEU-4 2.43 vs 2.17 in Table 2; augmentation 2.46 vs 2.17 in Table 4), ranking could easily be noise. Thus the paper's quantitative conclusions are not established for fully trained models. Secondary concern: augmentation protocols were selected by evaluating individual augmentations on the How2Sign test set (Section 4.4), which biases the reported augmentation gains; a validation split should be used for such selection.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper presents a systematic ablation study of pose-based preprocessing for sign language translation (SLT) with a T5 encoder-decoder transformer. On YouTubeASL training and How2Sign evaluation, it compares four normalization strategies (none, clip-wise, frame-wise, and a proposed SignSpace normalization), three missing-keypoint interpolation settings, and several augmentation protocols, reporting BLEU scores. The main quantitative result is that SignSpace normalization substantially outperforms no normalization (BLEU-4 2.17 vs 0.73). Interpolation and augmentation yield smaller improvements. The paper also provides qualitative analyses of encoder self-attention, cross-attention, and integrated gradients, and suggests that register tokens may improve performance. Code and preprocessed YouTubeASL data are released.","tokens_in":13977,"tokens_out":2583,"duration_ms":29655,"significance":"If the reported rankings are reliable, the study provides practically useful guidance for pose-based SLT preprocessing, particularly the SignSpace normalization, which shows a large gain. The attention analysis, especially the observation of spikes on non-informative frames, is an interesting qualitative contribution. The release of code and preprocessed data is a concrete asset for reproducibility. However, the central quantitative claims are weakened by the absence of training convergence evidence, the use of best-of-three-seed reporting without variance, and test-set-based selection of augmentations. The normalization result is large enough to likely survive these issues, but the interpolation and augmentation conclusions rest on small differences that could be noise.","major_comments":[{"comment":"The quantitative conclusions rest on an unverified assumption that 200,000-iteration models reflect the rankings of fully trained models. The paper explicitly states 'the trained models after 200,000 iterations are not fully trained' and asserts, without supporting evidence, that the comparative performance after this shorter protocol reflects the performance of fully-trained models. With a constant learning rate, different preprocessing schemes can plausibly converge at different rates, and a configuration that appears worse at 200k iterations could overtake another at convergence. The small differences in Tables 2 and 4 (e.g., BLEU-4 2.43 vs 2.17 and 2.46 vs 2.17) could easily reverse. Please provide evidence of ranking stability, such as training the key comparisons to convergence or showing learning curves across multiple checkpoints and seeds that demonstrate the rank order is stable.","section":"Section 4.1"},{"comment":"The augmentation protocol is selected based on test-set performance, which biases the reported gains. Table 3 evaluates individual augmentations on the How2Sign test set, and the text states 'Based on these individual performances, we select those augmentations that positively impact performance to design a final augmentation protocol.' The final protocols in Table 4 are then evaluated on the same test set. This selection procedure inflates the apparent benefit of augmentation. Please use a validation split for selecting augmentations and report test-set results only once, or otherwise account for the multiple-testing problem.","section":"Section 4.4"},{"comment":"Reporting only the best of three seeds without variance or significance testing makes it impossible to assess whether the observed differences are meaningful. The paper states 'we run each experiment with three different seeds and report the best run.' For differences as small as those in Tables 2 and 4, the reported point estimates could easily be within seed noise. Please report the mean and standard deviation (or all per-seed results) for at least the main comparisons, and ideally provide confidence intervals or significance tests. Without this, the abstract's claim that the techniques 'significantly improve' performance is not supported.","section":"Section 4.1 and Tables 1-4"},{"comment":"The abstract claims that attention analysis 'suggest[s] that adding a dedicated register token can improve overall model performance,' but no experiment in the paper tests register tokens. Section 5.2 only hypothesizes that register buffers might be helpful, and the conclusion says 'suggesting that register tokens could further enhance SLT performance.' This is a qualitative speculation, not a demonstrated result. Please soften the abstract wording to reflect that this is a hypothesis for future work, or add an actual experiment with a register token.","section":"Abstract and Section 5.2"}],"minor_comments":[{"comment":"The affiliation contains typos: 'Univerzity' should be 'University' and 'Faculty od Applied Sciences' should be 'Faculty of Applied Sciences.'","section":"Title page"},{"comment":"The sentence 'All the proposed normalization results in better performance' has a subject-verb agreement error; it should be 'All the proposed normalization methods result in better performance.'","section":"Section 4.2"},{"comment":"The statement 'the overall performance (majority of the BLEU scores) was improved by the shear, rotate elbow, and noise augmentations' is vague. Please specify which BLEU scores improved and by how much, since Table 3 shows mixed results across BLEU-1 to BLEU-4.","section":"Section 4.4"},{"comment":"The description of integrated gradients would benefit from specifying the baseline and the number of samples used in Figure 8. The text mentions a threshold of 0.3 for visualization but does not state how many samples were averaged or how 'relatively better translated' was operationalized beyond a minimal BLEU-1 score of 10.","section":"Section 5.3"},{"comment":"Reference [3] is described in the text as work on register buffers, but the citation appears as 'Bondarenko et al.' which is about quantizable transformers; please verify that the citation matches the claim about non-informative tokens.","section":"References"}],"recommendation":"major_revision","confidential_remarks":"The paper's central normalization finding is potentially valuable and the code/data release is a strength, but the quantitative case needs to be made reliable: convergence evidence, variance reporting, and a clean validation-based augmentation selection. These are fixable within the manuscript's scope, so I recommend major revision rather than rejection. The attention analysis is interesting but the register-token claim should be clearly labeled as a hypothesis unless an experiment is added."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague, the thing worth taking from this paper is the normalization result. SignSpace normalization gives BLEU-4 2.17 versus 0.73 with no normalization on How2Sign, and 1.13 for the frame-wise variant. That gap is large enough that I would trust it to survive noisy training, and it matches the sign-language intuition that keypoints should be normalized relative to the signing space, not the whole frame. If you build pose-based SLT systems, this is useful practical guidance. The code and preprocessed data are released, which makes the comparison reproducible, and the authors are honest that the model is not fully trained and that augmentation gains are mixed. That honesty is to their credit. What is new here is the systematic combination: T5-based pose SLT with SignSpace normalization, interpolation, and augmentation, all measured on YouTubeASL and How2Sign. I don't know of another paper that lays out these specific choices side by side. The attention analysis is suggestive — the cross-attention spikes on still, non-informative frames are a real observation and the register-token idea is plausible — but it is not tested, and the paper does not overclaim it. Now the soft spots. The stress-test note is right: the central assumption, that 200k-iteration rankings match fully-trained rankings, is load-bearing and unsupported. With a constant learning rate and no saturation in training curves, a configuration that looks worse at 200k could overtake another at convergence. This matters most for interpolation and augmentation, where the differences are small: 2.43 vs 2.17 BLEU-4 for interpolation, 2.46 vs 2.17 for medium augmentation, and many individual augmentation scores are within one or two tenths of the baseline. With only the best of three seeds reported and no error bars, those rankings could easily flip. The augmentation selection also happens on the test set — the authors choose which augmentations to keep based on How2Sign scores, then report those same scores as results. That is selection bias, and it weakens the augmentation conclusions further. For the normalization claim, the gap is big enough that these issues are unlikely to kill it. For interpolation and augmentation, the results are plausible but not established. Who is this for: practitioners in sign language translation who want concrete preprocessing guidance, and to a lesser extent researchers studying model interpretability in this area. It deserves a serious referee. The right outcome is a revise: report variance across seeds, use a validation split for augmentation selection, and either train longer or provide evidence that the 200k-iteration rankings are stable. I would send it out.","headline":"SignSpace normalization is a genuinely useful, likely robust finding, but the rest of the ablation ranking rests on an unvalidated convergence assumption and should be read as provisional.","tokens_in":14576,"tokens_out":1194,"would_cite":true,"duration_ms":16027,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper claims that normalizing pose keypoints to a shoulder-centered 'signing space' more than triples the BLEU-4 score of a T5-based sign language translation model, and that attention analysis suggests adding register tokens would…","keywords":["sign language translation","pose keypoints","T5 transformer","normalization","data augmentation","attention analysis","register tokens","ablation study"],"falsifier":"Train the same T5 model on YouTubeASL under each normalization scheme until validation BLEU saturates (or for several times longer than 200,000 iterations) and report the mean and standard deviation across seeds; if SignSpace does not clearly beat the frame-wise unit-box normalization, the headline claim fails. A second, direct test: actually add a register token to the encoder input as the paper proposes; if BLEU-4 does not improve over the no-register baseline, the attention-storage interpretation is not supported.","tokens_in":13505,"feed_emoji":"🤟","tokens_out":8004,"duration_ms":77062,"temperature":0.7,"pith_summary":"The paper argues that for gloss-free sign language translation from pose keypoints, how you preprocess the keypoints can matter as much as the model. Training a modified T5 encoder-decoder on YouTubeASL and evaluating on How2Sign without additional fine-tuning, the authors compare four normalization schemes, three missing-keypoint strategies, and several augmentation protocols. Their central quantitative claim is that 'SignSpace' normalization—a frame-wise normalization to a shoulder-centered signing box with locally normalized hands and face—raises BLEU-4 from 0.73 with no normalization to 2.17, with interpolation and a medium augmentation protocol adding smaller gains. The paper also claims, from attention and integrated-gradient analyses, that the model stores information in non-informative frames, which motivates adding dedicated register tokens.","feed_headline":"Signing-space pose normalization triples sign-translation BLEU","feed_subtitle":"Keypoints normalized to a shoulder-centered signing box lift BLEU-4 from 0.73 to 2.17 on How2Sign.","key_machinery":"The main computational object is SignSpace normalization: body pose keypoints are scaled so that the signing space—defined as a rectangle centered between the shoulders with width and height three times the shoulder distance—maps to the range [-1,1] with its center at [0,0], applied frame by frame, while hand and face keypoints are normalized locally with a 10% border. This scheme keeps global body relations intact while giving each articulator a focused, scale-invariant representation. Two supporting mechanisms carry the ablation results: linear interpolation of missing keypoints across short gaps (two frames or fewer), and a geometric augmentation set (shear, elbow rotation, additive noise) applied uniformly across frames. The attention insight rests on cross-attention spikes on non-informative frames, the phenomenon that motivates the paper's proposal of a register token—a dedicated learned input that would store such non-local information, following the register-buffer idea from vision transformers.","core_discovery":"The core discovery is that a linguistically motivated normalization—the 'signing space', a rectangle centered between the shoulders with width and height three times the shoulder distance—outperforms both clip-level and frame-level unit-box normalizations by a wide margin on the How2Sign benchmark. Combining SignSpace normalization, linear interpolation of missing keypoints for gaps up to two frames, and a medium-strength augmentation protocol of shear, elbow rotation, and additive noise yields the best observed BLEU-4 of 2.46, versus 0.73 with no normalization. The paper further shows, through averaged encoder self-attention and integrated gradients, that attention heads specialize in temporal context (past, current, future) and that the decoder consistently and strongly attends to non-informative frames, especially at the end of clips. This leads the authors to hypothesize that the model uses such frames as a memory store, analogous to register tokens in vision transformers, and that adding a dedicated register token could improve translation quality.","pith_inferences":["The SignSpace normalization gains likely transfer to other uncurated sign video datasets beyond YouTubeASL, since it directly targets signer position and scale variability; testing it on other backbones would show whether the effect is model-specific.","If the register-token hypothesis is correct, attention spikes on non-informative frames should disappear when a register token is added, providing a clean ablation of the storage mechanism.","The success of arm-elbow rotation and noise over global rotation suggests a principle: for sign data, augmentations should preserve the articulatory geometry of the sign rather than the camera geometry; a principled taxonomy of sign-safe augmentations would extend this.","The observation of models beating weak references implies that the YouTubeASL caption labels contain systematic errors; an automated pseudo-labeling pipeline that detects and corrects such errors could turn a training-data weakness into a training signal."],"forward_implications":["Signing-space normalization is a cheap, model-agnostic preprocessing step that any pose-based sign translation system could adopt, with a threefold BLEU improvement observed here.","Interpolating short gaps of missing keypoints instead of filling them with a constant should become the default for pose-based sign language translation.","A medium-strength augmentation protocol using shear, elbow rotation, and noise outperforms heavy or no augmentation, while rotation and perspective can degrade translation.","The attention analysis suggests that pose-based transformers exploit non-informative frames as information storage; adding register tokens is a concrete and testable next step.","The model sometimes produces translations that are more correct than the weak YouTubeASL references, which implies that re-labeling or pseudo-labeling the training captions could yield further gains."],"supporting_citations":[{"why":"Provides the YouTubeASL training dataset and the clip-level (yaslc) and frame-level (yaslf) normalization baselines that the paper compares against.","marker":"[35]"},{"why":"Supplies the How2Sign dataset and its default split, used as the zero-finetuning evaluation benchmark for all BLEU scores.","marker":"[10]"},{"why":"Defines the T5 architecture that the paper modifies with a keypoint embedding layer and fine-tunes for sign language translation.","marker":"[30]"},{"why":"Introduces the signing-space concept and the sign pose-based transformer approach on which the SignSpace normalization is built.","marker":"[2]"},{"why":"MediaPipe is the keypoint extractor producing the 104 body, hand, and face keypoints that form the model input.","marker":"[26]"},{"why":"YOLOv8-nano is used for initial person detection to locate the signer before keypoint prediction and clip filtering.","marker":"[19]"},{"why":"The 'vision transformers need registers' work supplies the register-token analogy that motivates the paper's hypothesis for improving SLT.","marker":"[8]"},{"why":"Provides evidence of attention heads acting as no-op or outlier-storage mechanisms, supporting the register-buffer interpretation of cross-attention spikes.","marker":"[3]"},{"why":"The SignAttention study documents transformer attention clustering in sign language translation, which the paper's integrated-gradient analysis aligns with.","marker":"[1]"},{"why":"The Captum library is used to compute integrated gradients for the attribution analysis of pose frames to generated tokens.","marker":"[21]"}],"fun_headline_variants":["Signing-space normalization triples sign translation BLEU","Shoulder-centered signing box lifts BLEU 3x on How2Sign","Keypoints in signing space triple BLEU for sign language","Linguistic keypoint normalization boosts sign BLEU to 2.46","Signing-space preprocessing triples sign-translation BLEU"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The paper's ablations compare models trained for a fixed 200,000 iterations that the authors themselves say are not fully trained, with only the best of three seeds reported; if different preprocessing variants converge at different speeds, the ranking of normalization, interpolation, and augmentation strategies may not hold for fully trained models.","fun_headline_variants_meta":{"raw":{"variants":["Signing-space normalization triples sign translation BLEU","Shoulder-centered signing box lifts BLEU 3x on How2Sign","Keypoints in signing space triple BLEU for sign language","Linguistic keypoint normalization boosts sign BLEU to 2.46","Signing-space preprocessing triples sign-translation BLEU"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000308,"raw_usage":{"total_tokens":1733,"prompt_tokens":887,"completion_tokens":846,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":503,"completion_tokens_details":{"reasoning_tokens":753}},"tokens_in":503,"tokens_out":846,"duration_ms":8718,"temperature":1.0,"reasoning_tokens":753,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T20:48:34.742584+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Train the same T5 model on YouTubeASL under each normalization scheme until validation BLEU saturates (or for several times longer than 200,000 iterations) and report the mean and standard deviation across seeds; if SignSpace does not clearly beat the frame-wise unit-box normalization, the headline claim fails. A second, direct test: actually add a register token to the encoder input as the paper proposes; if BLEU-4 does not improve over the no-register baseline, the attention-storage interpretation is not supported.","supporting_citations":[{"cited_title":"Y outube- asl: A large-scale, open-domain american sign language- english parallel corpus, 2023","cited_arxiv_id":null,"evidence_quote":"Provides the YouTubeASL training dataset and the clip-level (yaslc) and frame-level (yaslf) normalization baselines that the paper compares against."},{"cited_title":"How2sign: A large-scale multi- modal dataset for continuous american sign language, 2021","cited_arxiv_id":null,"evidence_quote":"Supplies the How2Sign dataset and its default split, used as the zero-finetuning evaluation benchmark for all BLEU scores."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Defines the T5 architecture that the paper modifies with a keypoint embedding layer and fine-tunes for sign language translation."},{"cited_title":"Sign pose-based trans- former for word-level sign language recognition","cited_arxiv_id":null,"evidence_quote":"Introduces the signing-space concept and the sign pose-based transformer approach on which the SignSpace normalization is built."},{"cited_title":"Mediapipe: A 9 framework for perceiving and processing reality","cited_arxiv_id":null,"evidence_quote":"MediaPipe is the keypoint extractor producing the 104 body, hand, and face keypoints that form the model input."},{"cited_title":"Vision transformers need registers, 2024","cited_arxiv_id":null,"evidence_quote":"The 'vision transformers need registers' work supplies the register-token analogy that motivates the paper's hypothesis for improving SLT."},{"cited_title":"Quantizable transformers: Removing outliers by helping attention heads do nothing","cited_arxiv_id":null,"evidence_quote":"Provides evidence of attention heads acting as no-op or outlier-storage mechanisms, supporting the register-buffer interpretation of cross-attention spikes."},{"cited_title":"SignAttention: On the Interpretability of Transformer Models for Sign Language Translation","cited_arxiv_id":"2410.14506","evidence_quote":"The SignAttention study documents transformer attention clustering in sign language translation, which the paper's integrated-gradient analysis aligns with."}],"review_version":1}