Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models
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Computer Science > Computation and Language
Title:Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models
Abstract:Sign language translation (SLT) remains constrained by limited paired sign-video/text corpora and heavy-tailed target vocabularies. We study target-side augmentation in which GPT-4o generates controlled paraphrase variants of reference sentences while the sign input remains unchanged. 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.
We evaluate on three datasets spanning complementary challenges: PHOENIX14T (German Sign Language), with moderate lexical diversity; GSL (Greek Sign Language), with highly ontrolled, repetitive recordings; and LSA-T (Argentinian Sign Language), with severe long-tail sparsity. On PHOENIX14T, augmentation improves BLEU-4 from 9.56 to 10.33. The near-saturated GSL baseline and extremely sparse LSA-T setting reveal the limits of the approach. To our knowledge, this is the first study to apply LLM-generated target-side araphrases and LLM-as-a-Judge evaluation to SLT. The semantic evaluation reveals gains in fidelity that lexical overlap metrics understate.
| Comments: | Accepted at GenSign (this https URL) at CVPR 2026. Non proceedings track |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.31393 [cs.CL] |
| (or arXiv:2605.31393v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.31393
arXiv-issued DOI via DataCite (pending registration)
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