摘要
arXiv:2605.31393v1 Announce Type: new 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.