pith. sign in

arxiv: 2605.20588 · v1 · pith:R3KUMINXnew · submitted 2026-05-20 · 💻 cs.CL · cs.CV

Direct Translation between Sign Languages

Zetian Wu , Bowen Xie , Wuyang Meng , Milan Gautam , Stefan Lee , Liang Huang This is my paper

Pith reviewed 2026-05-21 05:50 UTC · model grok-4.3

classification 💻 cs.CL cs.CV
keywords sign language translationdirect sign-to-signback-translationsynthetic parallel dataASLCSLDGS
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The pith

A unified model trained on synthetic sign-sign pairs translates directly between sign languages more accurately and faster than routing through text.

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

The paper aims to let users of different sign languages communicate directly without spoken-language text or human interpreters, which would remove a major barrier for deaf communities. It generates synthetic sign-to-sign training pairs from separate unaligned sign-text collections by back-translating through text, then trains one model to handle both text-to-sign and sign-to-sign tasks at once. This direct route avoids the compounding mistakes and extra steps of a three-part cascade, producing sign sequences that are geometrically closer to real signs and that match the intended meaning better when checked by translation back to text.

Core claim

Using back-translation to create synthetic sign-sign pairs from unaligned sign language corpora, a single MBART-based model jointly trained for text-to-sign and sign-to-sign translation outperforms a cascaded sign-to-text-to-sign baseline, with 20 percent lower DTW-aligned MPJPE on geometric metrics, 50 percent higher BLEU-4 after back-translation to sentences, and roughly 2.3 times faster inference, with similar gains observed on a small existing cross-lingual sign set.

What carries the argument

Synthetic sign-sign pairs produced by back-translation from separate sign-text corpora, used to jointly train a single model for direct sign-to-sign mapping.

If this is right

  • The direct method records 20 percent lower DTW-aligned MPJPE than the cascade on geometric sign error.
  • After translating model outputs back to text, it reaches 50 percent higher BLEU-4 than the cascaded route.
  • Inference runs approximately 2.3 times faster than the three-stage cascade.
  • Comparable accuracy and speed gains appear on a small existing set of real cross-lingual sign data.

Where Pith is reading between the lines

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

  • If the synthetic-pair approach scales, it could support direct translation for additional sign language pairs that lack parallel data.
  • Direct modeling may retain visual features such as facial expression and spatial grammar that text intermediaries discard.
  • Applying the same back-translation recipe to larger unaligned corpora could test whether performance keeps improving with more synthetic data.

Load-bearing premise

The synthetic sign-sign pairs created by back-translation are close enough in visual form and meaning to real parallel data that joint training improves results without hiding systematic mismatches the chosen metrics cannot detect.

What would settle it

Evaluating the direct model against the cascade on a sizable collection of human-collected, time-aligned parallel sign utterances between two sign languages and finding no consistent advantage for the direct method.

Figures

Figures reproduced from arXiv: 2605.20588 by Bowen Xie, Liang Huang, Milan Gautam, Stefan Lee, Wuyang Meng, Zetian Wu.

Figure 1
Figure 1. Figure 1: Direct sign-to-sign translation. Given a source clip in one sign language (e.g. CSL), our single MBART-based model produces an equivalent clip in a target sign language (e.g. ASL) without going through written text. Compared with the cascaded S2T → MT → T2S baseline, the direct model is roughly 2.3× faster and yields lower DTW-aligned MPJPE. parallel training set is very small (at most 2.3K S2S pairs in on… view at source ↗
Figure 2
Figure 2. Figure 2: Back-translation: NMT and signs side by side. (a) Standard NMT back-translation [27] introduced in 2.1 (b) Our cross-lingual sign back-translation introduced in 2.2 model learns to recover a clean target from a noisy input rather than to imitate a noisy supervision signal. Edunov et al. [6] subsequently showed that the technique scales to hundreds of millions of monolingual sentences and consistently impro… view at source ↗
Figure 3
Figure 3. Figure 3: Model overview. A single MBART-based encoder–decoder handles both T2S and S2S, with the input/output language signaled by special tokens at the encoder input and decoder prefix. For T2S the encoder reads a spoken-language text; for S2S it reads a sign clip encoded into discrete motion tokens via a VQ-VAE. In both cases the decoder emits motion tokens autoregressively, which are decoded back to motion by th… view at source ↗
Figure 4
Figure 4. Figure 4: CSL-ASL pairs from the strict subset. One removed pair also shown for comparison. We evaluate on three sign↔sign test sets. The BT-input set consists of held-out (text, sign) pairs from each source corpus, passed through the same back-translation pipeline used at train￾ing time (§2): the held-out text is translated and fed to our T2S model to produce a synthetic source clip, which is paired with the gold t… view at source ↗
read the original abstract

The field of sign language translation has witnessed significant progress in the translation between sign and spoken languages, but the translation between sign languages remains largely unexplored and out of reach. The latter can help 1.5 billion deaf and hard-of-hearing (DHH) people worldwide communicate across language barriers without relying on hearing interpreters or written-language fluency. The cascade approach composing separate sign-to-text, text-to-text, and text-to-sign systems suffers from error propagation and extra latency as well as the loss of information unique in the visual modality. We aim to develop direct sign-to-sign translation. However, a large-scale open-domain parallel corpus has not been curated between sign languages. To enable direct translation between sign language utterances, we use back-translation to produce synthetic sign-sign pairs from unaligned individual language utterance-sign corpora. Using this data, we jointly train a single MBART-based model for both text->sign (T2S) and sign->sign (S2S). On synthetically generated paired sets between American Sign Language (ASL), Chinese Sign Language (CSL), and German Sign Language (DGS), our direct S2S method outperforms the cascaded baseline on geometric sign error metrics (20% lower DTW-aligned MPJPE) and language matching metrics after predicted sign utterances are translated back to sentences (50% high BLEU-4) while achieving a roughly 2.3* speedup. On a small set of pre-existing cross-lingual sign data, we find similar improvements for our proposed method.

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.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes direct sign-to-sign (S2S) translation between ASL, CSL, and DGS by using back-translation on unaligned sign-text corpora to synthesize parallel sign-sign pairs, then jointly training a single MBART-based model for both text-to-sign (T2S) and S2S tasks. It reports that this direct approach outperforms a cascaded sign-to-text + text-to-text + text-to-sign baseline on synthetic pairs with 20% lower DTW-aligned MPJPE, 50% higher BLEU-4 (after back-translating predictions to text), and 2.3× speedup, with similar trends on a small pre-existing cross-lingual sign dataset.

Significance. If the synthetic pairs faithfully preserve both geometric sign content and linguistic intent without systematic artifacts, the work could enable practical direct sign-language translation that avoids cascade error propagation, extra latency, and loss of visual-modality information, directly benefiting cross-lingual communication for DHH populations. The joint MBART training and reported speed-up are attractive engineering contributions, but the significance is conditional on independent verification that performance gains are not artifacts of the back-translation process.

major comments (3)
  1. [Abstract] Abstract: the language-matching claim (50% higher BLEU-4) is obtained by translating predicted sign utterances back to text before scoring. This re-introduces the text modality the method is designed to bypass and creates partial circularity in the metric, even though the geometric DTW-aligned MPJPE remains independent.
  2. [Synthetic data generation] Synthetic data generation (described in the abstract and method): no validation of back-translation quality is reported (e.g., human semantic-equivalence ratings or geometric consistency checks on the generated sign-sign pairs themselves). Without such checks, the 20% MPJPE reduction and BLEU gains could arise from artifacts or simplifications in the synthetic pairs that the joint T2S+S2S model exploits while the cascade does not.
  3. [Experiments] Experiments section: results are presented without error bars, statistical significance tests, or ablations on the synthetic-data generation process (including the back-translation sampling temperature listed as a free parameter). This leaves the robustness of the central outperformance claim difficult to assess.
minor comments (2)
  1. [Abstract] Abstract: '50% high BLEU-4' appears to be a typo and should read '50% higher BLEU-4'.
  2. [Abstract] Abstract: '2.3* speedup' should be written as '2.3× speedup' for typographic clarity.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for their constructive comments on our work. We address each of the major comments point by point below, indicating where revisions will be made to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the language-matching claim (50% higher BLEU-4) is obtained by translating predicted sign utterances back to text before scoring. This re-introduces the text modality the method is designed to bypass and creates partial circularity in the metric, even though the geometric DTW-aligned MPJPE remains independent.

    Authors: We concur that relying on back-translation to text for the BLEU-4 score does reintroduce the text modality and introduces a degree of circularity in that particular metric. The geometric evaluation via DTW-aligned MPJPE is independent of text and demonstrates a clear 20% improvement for the direct method. In the revised manuscript, we will update the abstract to emphasize the geometric metric as the primary result and provide additional clarification on the evaluation procedure for language matching. We view this as a partial revision to better frame the claims. revision: partial

  2. Referee: [Synthetic data generation] Synthetic data generation (described in the abstract and method): no validation of back-translation quality is reported (e.g., human semantic-equivalence ratings or geometric consistency checks on the generated sign-sign pairs themselves). Without such checks, the 20% MPJPE reduction and BLEU gains could arise from artifacts or simplifications in the synthetic pairs that the joint T2S+S2S model exploits while the cascade does not.

    Authors: The referee is correct that we did not report any validation of the synthetic sign-sign pairs, such as human ratings for semantic equivalence or checks for geometric consistency. This omission means we cannot fully exclude the possibility that the performance gains are due to artifacts in the synthetic data. We will revise the manuscript to include a limitations section discussing this issue and add qualitative examples of the generated pairs along with any available consistency metrics. However, comprehensive human evaluations are resource-intensive and not included in this revision. revision: partial

  3. Referee: [Experiments] Experiments section: results are presented without error bars, statistical significance tests, or ablations on the synthetic-data generation process (including the back-translation sampling temperature listed as a free parameter). This leaves the robustness of the central outperformance claim difficult to assess.

    Authors: We agree that the lack of error bars, statistical tests, and ablations on parameters like the back-translation sampling temperature weakens the assessment of result robustness. We will incorporate error bars from multiple training runs, conduct statistical significance tests where appropriate, and add ablations varying the sampling temperature to the experiments section. These changes will be made in the revised version. revision: yes

standing simulated objections not resolved
  • Comprehensive human semantic-equivalence ratings for the synthetic sign-sign pairs would require recruiting qualified sign language interpreters or native signers for annotation, which is not feasible within the timeline and resources available for this revision.

Circularity Check

0 steps flagged

No circularity detected in empirical sign-to-sign translation method

full rationale

The paper presents an applied empirical pipeline: back-translation generates synthetic sign-sign pairs from unaligned sign-text corpora, a single MBART model is jointly trained for T2S and S2S, and performance is measured via independent geometric metrics (DTW-aligned MPJPE) plus BLEU after an auxiliary sign-to-text step. No mathematical derivation, first-principles result, or equation chain is claimed that reduces to its own inputs by construction. No parameters are fitted on a subset and then relabeled as predictions, no self-citation load-bearing uniqueness theorems appear, and no ansatz is smuggled via prior work. The evaluation choices and data-generation technique are standard in machine translation and do not create self-definitional or tautological equivalences; results are reported as experimental outcomes on held-out synthetic and pre-existing sets.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the quality of back-translation synthetic pairs and the assumption that a single sequence model can jointly handle text and sign modalities without modality-specific losses. No new physical entities are postulated. Free parameters are the standard MBART hyperparameters and any back-translation sampling choices.

free parameters (1)
  • back-translation sampling temperature
    Controls diversity of synthetic sign-sign pairs; value not stated in abstract but required for reproducibility.
axioms (1)
  • domain assumption Back-translation from unaligned sign-text corpora produces pairs whose visual content is faithful enough for downstream training.
    Invoked when the authors state they use back-translation to produce synthetic sign-sign pairs.

pith-pipeline@v0.9.0 · 5809 in / 1383 out tokens · 35879 ms · 2026-05-21T05:50:45.330004+00:00 · methodology

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Reference graph

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