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arxiv: 2605.31393 · v2 · pith:SE4NBQ6Gnew · submitted 2026-05-29 · 💻 cs.CL · cs.AI

Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models

Pedro Dal Bianco , Jean Paul Nunes Reinhold , Oscar Stanchi , Facundo Quiroga , Franco Ronchetti , Ulisses Brisolara Corr\^ea This is my paper

Pith reviewed 2026-06-28 22:26 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords sign language translationparaphrase augmentationlarge language modelstarget-side augmentationBLEU evaluationsemantic evaluationPHOENIX14Tgeneralization
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The pith

Using an LLM to generate paraphrases of target sentences improves BLEU-4 scores for sign language translation on the PHOENIX14T dataset.

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

The paper tests whether augmenting the text targets with multiple semantically equivalent paraphrases generated by GPT-4o can help sign language translation models learn to generalize when training data is limited. The sign videos stay the same while the model sees several ways to phrase the same meaning during an initial training phase before fine-tuning on the original sentences. On the PHOENIX14T German Sign Language dataset this produces a BLEU-4 gain from 9.56 to 10.33, while results on other datasets show the approach has limits when data is either too repetitive or too sparse. A sympathetic reader would care because gathering additional sign language video data is costly, so making better use of existing pairs through text variation could improve real-world performance without new recordings.

Core claim

Pre-training a Signformer-style model on LLM-generated controlled paraphrase variants of the reference sentences, followed by fine-tuning on the original references, raises BLEU-4 from 9.56 to 10.33 on PHOENIX14T by helping the decoder move beyond memorizing single reference phrasings, with complementary LLM-as-a-Judge evaluation revealing semantic gains that lexical metrics understate.

What carries the argument

Target-side augmentation that keeps the sign input fixed while replacing each reference sentence with multiple LLM-generated semantically faithful paraphrase variants during a two-stage pre-training and fine-tuning schedule.

If this is right

  • The decoder generalizes beyond memorized reference phrasing on datasets with moderate lexical diversity.
  • Lexical overlap metrics alone are insufficient to capture the full performance picture in SLT.
  • The approach shows limited benefit on near-saturated repetitive datasets and on extremely sparse large-vocabulary corpora.
  • Semantic evaluation protocols become necessary to assess true fidelity gains.

Where Pith is reading between the lines

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

  • The same target-side strategy could be tested on other low-resource spoken language translation tasks facing similar data scarcity.
  • Dataset characteristics such as lexical diversity determine whether paraphrase augmentation yields measurable gains.
  • The limits observed on repetitive and sparse datasets suggest the method works best as a complement to other data collection efforts.

Load-bearing premise

The paraphrases produced by GPT-4o are semantically faithful to the originals and supply a useful training signal that improves generalization instead of adding noise or bias.

What would settle it

Training the identical model architecture and schedule on PHOENIX14T without the paraphrase augmentation step and finding that the BLEU-4 score does not exceed 9.56.

Figures

Figures reproduced from arXiv: 2605.31393 by Facundo Quiroga, Franco Ronchetti, Jean Paul Nunes Reinhold, Oscar Stanchi, Pedro Dal Bianco, Ulisses Brisolara Corr\^ea.

Figure 1
Figure 1. Figure 1: Overview of the adapted Signformer architecture (orig￾inally from [17]) used in our experiments. Instead of the CNN￾extracted frame tokens in the original, each frame’s concatenated hand, upper-body, and selected facial landmarks (normalized and linearly projected) feed the encoder directly as pose keypoints. During training, these are materialized as four examples (V, T), (V, T′ 1 ), (V, T′ 2 ), (V, T′ 3 … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the LLM-augmented SLT pipeline. For each video–text pair [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
read the original abstract

Sign language translation (SLT) remains constrained by the limited availability of paired sign-video/text corpora and by the heavy-tailed vocabularies typical of real-world datasets. We study a target-side augmentation strategy in which a large language model (LLM) generates controlled paraphrase variants of the reference spoken-language sentence while the sign input remains unchanged. Concretely, we use GPT-4o to produce semantically faithful variants of the training targets and train a Signformer-style pose-based Transformer under a two-stage schedule: pre-training on the augmented corpus followed by fine-tuning on the original references. We evaluate this strategy on three datasets that span complementary challenges: PHOENIX14T (German Sign Language), a real-world corpus with moderate lexical diversity; the Greek Sign Language Dataset with highly controlled, repetitive recordings; and LSA-T (Argentinian Sign Language), a naturalistic corpus with a large vocabulary and severe long-tail sparsity. This range allows us to characterize precisely when and why target-side augmentation is beneficial. On PHOENIX14T, augmentation improves BLEU-4 from 9.56 to 10.33, demonstrating that paraphrastic exposure helps the decoder generalize beyond memorized reference phrasing. The near-saturated GSL baseline and the extremely sparse LSA-T setting reveal the limits of the approach: in both cases, single-reference lexical overlap metrics are insufficient to capture the full picture, motivating a complementary semantic evaluation. To our knowledge, this is the first study to examine LLM-generated target-side paraphrases as an augmentation mechanism for SLT, and the first to apply an LLM-as-a-Judge evaluation protocol to SLT. This complementary evaluation reveals gains in semantic fidelity that lexical overlap metrics understate.

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

2 major / 1 minor

Summary. The manuscript proposes target-side paraphrase augmentation for sign language translation: GPT-4o generates controlled, semantically faithful variants of reference spoken-language sentences while sign-video inputs remain fixed. A Signformer-style pose-based Transformer is trained under a two-stage schedule (pre-training on the augmented corpus, followed by fine-tuning on the original references). Experiments span PHOENIX14T (German SL), the Greek Sign Language Dataset, and LSA-T (Argentinian SL). The central empirical result is a BLEU-4 improvement from 9.56 to 10.33 on PHOENIX14T; the work also reports that single-reference lexical metrics are insufficient on the other two datasets and introduces an LLM-as-Judge semantic evaluation protocol. The authors position the study as the first to examine LLM-generated target-side paraphrases for SLT.

Significance. If the reported BLEU gain is reproducible and the generated paraphrases are shown to preserve meaning, the approach offers a practical route to mitigate data scarcity and heavy-tailed vocabularies in SLT by increasing target-side lexical diversity without modifying the visual input. The multi-dataset design and complementary semantic evaluation are strengths that help delineate when the method is beneficial versus when lexical-overlap metrics fail to capture gains.

major comments (2)
  1. [Abstract] Abstract: the central claim that the BLEU-4 gain (9.56 o 10.33) demonstrates improved decoder generalization via paraphrastic exposure rests on the unverified assertion that GPT-4o outputs are 'semantically faithful variants' and 'controlled paraphrase variants.' No supporting measurement—embedding cosine, NLI entailment rate, or human semantic-equivalence ratings—is reported for the actual training paraphrases, so it is impossible to rule out that the observed improvement arises from training on noisy or meaning-shifted targets rather than beneficial exposure.
  2. [Abstract] Abstract: the two-stage schedule (pre-training on augmented data, fine-tuning on originals) is presented without an ablation that isolates the contribution of each stage or of the augmentation itself; likewise, no statistical significance, error bars, or dataset-split details are supplied, making it impossible to assess whether the numeric gain is load-bearing evidence for the proposed mechanism.
minor comments (1)
  1. [Abstract] The abstract states that the three datasets 'span complementary challenges' but does not specify the exact train/dev/test splits or vocabulary statistics used for each, which would aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments below and will revise the manuscript accordingly to strengthen the empirical support for our claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the BLEU-4 gain (9.56 o 10.33) demonstrates improved decoder generalization via paraphrastic exposure rests on the unverified assertion that GPT-4o outputs are 'semantically faithful variants' and 'controlled paraphrase variants.' No supporting measurement—embedding cosine, NLI entailment rate, or human semantic-equivalence ratings—is reported for the actual training paraphrases, so it is impossible to rule out that the observed improvement arises from training on noisy or meaning-shifted targets rather than beneficial exposure.

    Authors: We agree that explicit verification of semantic faithfulness for the generated paraphrases is necessary to support the central claim. The revised manuscript will include quantitative measurements on the training paraphrases: sentence embedding cosine similarity (using a multilingual model) and NLI entailment rates between originals and GPT-4o outputs. These will be reported alongside the prompt templates used to enforce control and faithfulness. We will also note any cases where paraphrases were filtered for semantic drift. revision: yes

  2. Referee: [Abstract] Abstract: the two-stage schedule (pre-training on augmented data, fine-tuning on originals) is presented without an ablation that isolates the contribution of each stage or of the augmentation itself; likewise, no statistical significance, error bars, or dataset-split details are supplied, making it impossible to assess whether the numeric gain is load-bearing evidence for the proposed mechanism.

    Authors: We acknowledge that the current version lacks ablations isolating the two-stage schedule and the augmentation effect, as well as statistical details. In revision we will add: (i) an ablation comparing the full two-stage procedure against single-stage training on augmented data only and against the unaugmented baseline; (ii) results averaged over multiple random seeds with error bars and paired statistical significance tests (e.g., bootstrap or t-test); and (iii) explicit documentation of the train/validation/test splits for all three datasets. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical augmentation results independent of inputs

full rationale

The paper reports an empirical experiment: GPT-4o generates target paraphrases, a Transformer is trained under a two-stage schedule, and BLEU-4 is measured on PHOENIX14T (9.56 → 10.33) plus other datasets. No equations, fitted parameters, or derivations appear. The central claim is a direct experimental outcome, not a prediction that reduces to its own inputs by construction. The assumption that generated variants are semantically faithful is an unverified modeling choice, but it is not circular per the enumerated patterns; it would fall under correctness risk instead. No self-citation chains, ansatzes, or renamings are load-bearing. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract. The central claim rests on the unverified domain assumption that the LLM outputs remain semantically faithful and beneficial for training.

pith-pipeline@v0.9.1-grok · 5867 in / 1167 out tokens · 40701 ms · 2026-06-28T22:26:47.813627+00:00 · methodology

discussion (0)

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Reference graph

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