In this paper, we report our system for SemEval-2026 Task 5, which predicts graded plausibility scores for target word senses in narrative context. We explore embedding-based similarity, transformer fine tuning, and a three-stage curriculum combining WiC pretraining, Wasserstein distribution learning, and KL-based calibration. Our best model, DeBERTa-XLarge with curriculum training, achieves 78% accu-racy within one standard deviation and a Spear-man correlation of 0.70, with an overall test score of 0.74. Results show that distribution modeling better aligns with human plausibility judgments than single-score prediction

Burushaski is a language isolate spoken in northern Pakistan with a predominantly oral tradition, limited standardized orthography, and virtually no existing speech technology infrastructure. These characteristics make conventional text-centric NLP pipelines unsuitable and position speech data collection as the primary scientific challenge. This paper introduces an audio-first, linguistically informed methodology for developing a Burushaski–English speech translation resource. Rather than prioritizing model architecture, we focus on principled corpus design tailored to the language’s morphological complexity, ergative-absolutive alignment, and four-gender agreement system. The dataset combines structured elicitation targeting high-frequency and morphologically diverse constructions, functional and formulaic speech, and oral narratives that capture discourse-level phenomena. We describe the design of a custom data collection application, community-embedded crowdsourcing strategy, and translation-aligned workflow for generating parallel speech–English data. The resulting pilot corpus comprises approximately 10 hours of curated audio from 42 speakers across controlled and naturalistic settings. While we present the results of preliminary translation experiments using Whisper, the primary contribution of this work is methodological: a scalable framework for speech-first corpus development in morphologically rich, under-resourced, and predominantly oral languages. We argue that for languages lacking stable orthography and large textual corpora, data design, not model selection constitutes the central research problem