The WSLP 2026 shared task studies sentence-level translation from Indian Sign Language pose sequences to English, with training on iSign and evaluation on a disjoint collection of source videos. We address two challenges in this setting: unstable pose scale caused by landmark errors and weak grounding between pose inputs and generated text. We introduce robust clip-level normalization and shared-encoder contrastive grounding, which aligns pose and target-text representations in a pretrained T5 encoder during training while retaining standard autoregressive decoding at inference. On a video-held-out iSign development split, contrastive grounding improves chrF from 0.102 to 0.121, while clip-level normalization further improves it to 0.128; the normalization gain is consistent across three matched seeds. A retrieval probe provides complementary evidence of improved pose–text alignment. Our final shared-task submission achieves chrF 0.16 on the official test set. However, input-independent English controls reach chrF 0.15, showing that small differences in the official score are difficult to interpret at the current level of translation accuracy. The source code is available at https://github.com/gyan-shrestha/WSLP-2026-SLT.

Expert annotation is a central bottleneck in sign-language corpus construction. A natural assumption is that annotation budgets should prioritize diverse or underrepresented signing patterns. We test this in a controlled retrospective study on PHOENIX14T (German Sign Language) and Isharah (Saudi Sign Language), across gloss recognition, translation, multiple budgets, and three seeds, comparing random sampling against geometric, active-learning, pose-derived articulatory, and privileged selectors. Articulatory coverage is generally weak across all four corpus–task settings and rarely outperforms random sampling. A density analysis shows it selects locally atypical clips even after controlling for duration; removing the sparsest 10–20% helps at larger budgets but never enough to beat random. A privileged gloss-diversity oracle also performs poorly despite selecting comparatively typical clips, showing that atypicality is not the whole explanation. Facility-location representativeness is the strongest feasible selector in three of four largest-budget settings, with lower WER than random in 16 of 18 seed-matched recognition comparisons, compared with only 2 of 18 for articulatory-only selection. The results suggest pose-derived articulatory coverage and gloss-type coverage, as operationalized here, are unreliable proxies for annotation utility in low-resource sign-language corpus construction. The source code is available at https://github.com/gyan-shrestha/WSLP-2026.