Cross-lingual transfer is a standard approach to low-resource sign language translation, but it is unclear what the transferred model actually provides. We fine-tune a pose-based model from American Sign Language (ASL) to Indian Sign Language (ISL) and decompose the resulting transfer across its encoder, decoder, and articulator streams. The matched ASL encoder-decoder pair reaches 3.61 BLEU-4 on ISL, while either component alone performs close to training from scratch. Content-word recall reveals a sharp frequency effect: frequently observed content improves with training and additional data, whereas rare and unseen content remains near zero, even after twenty epochs and a 49% increase in training data. Finally, comparing ASL and Chinese Sign Language (CSL) sources shows that the apparent source-language gap is substantially reduced when the output language is controlled, with remaining differences concentrated differently across articulators. Because the source checkpoints also differ in pretraining domain, we treat this final pattern as suggestive rather than causal. Together, the results show that transfer provides a coupled encoder-decoder starting point, but does not by itself solve the long tail of target-language content.

We describe ENSAR, our submission to the AraSeg 2026 shared task on Arabic sentence segmentation, where a system must decide, for every word in a document, whether a sentence ends there. The four tracks strip punctuation, paragraph breaks, both, or neither. We start from an ensemble of five fine-tuned encoders decoded with a length-aware semi-Markov program. It is strong, but most of its mistakes are places where all five models are wrong and all five assign high confidence, so averaging them or moving a threshold changes nothing. ENSAR adds a reasoning stage on top. Two LLM juries, each holding a written policy learned from its own graded errors on the training split, debate every document and apply only the edits both endorse. Our system ranked first on three of the four tracks in both the closed and open settings.