Sign spotting, the task of deciding whether an isolated query sign occurs inside a clip of continuous signing, enables sign language video retrieval without gloss or text annotation. We describe our system for the WSLP@EMNLP-2026 Word/Sign Presence Prediction shared task on Indian Sign Language: a lightweight (2.86M-parameter) pose-based Siamese encoder that embeds both clips in one space, forms an explicit frame-to-frame cosine similarity map, and reduces it with multiple-instance pooling over temporal windows. Training combines binary cross-entropy with a multiple-instance contrastive objective (MIL-NCE). The system reaches 72.69 accuracy and 71.95 macro-F1 on the blind test set, against 61.66 and 58.42 for the published pose baseline on the same data. Ablations identify contrastive alignment as the component that makes the architecture work: the MIL-NCE objective is what places the two clips in a comparable metric space, and training does not leave chance without it. We further document a label-balance property of the released splits that fixes 76.3% of blind test labels without a model, and recommend a change for future editions.

We address cross-signer Isolated Sign Language Recognition (ISLR) for Indian Sign Language as part of the WSLP-2026 shared task, where most target words have exactly one available training video and a system must recognize a query video performed by a signer never seen for that class during training. We approach this with a metric-learning encoder trained in two phases: pretraining on a large, multi-signer American Sign Language corpus to learn general signer-invariance, then fine-tuning on the target vocabulary using every real cross-signer recording we can responsibly source. Our preprocessing detects and extracts multiple performances of a word within a single recording as separate training samples, a technique not previously reported in SLR literature, and adapts a published missing-keypoint interpolation technique into a retrieval rather than classification pipeline. Our final system achieves 67.11% Top-1, 69.20% Top-5, and 70.34% Top-10 accuracy on the WSLP-2026 test-phase evaluation.

Large language models excel at technical problem solving in English but struggle when questions are posed in Bangla. While translation offers a practical solution, existing Bangla-English systems frequently mistranslate specialized terminology, altering problem semantics and degrading downstream performance. We present BanglaSTEM, a dataset of 5,000 Bangla-English sentence pairs covering computer science, mathematics, physics, chemistry, and biology. Our pipeline extracts matching passages from official bilingual curriculum textbooks using OCR, then uses LLMs to align sentences and mark technical terms. These aligned examples serve as few-shot prompts for generating over 12,000 new translation pairs from LLMs, avoiding copyright issues. Human evaluators then select the best 5,000 pairs that correctly preserve technical terminology. We also test a term-weighted BLEU metric that gives higher weight to technical words, since standard metrics treat terminology errors and common word errors equally. We show that our weighted metric correlates better with downstream accuracy in code generation and math solving, while standard BLEU gives high scores even for wrong translations. The full implementation, dataset, and model will be made publicly available.

We present a hybrid approach for Bangla hate speech detection that combines linguistic analysis with neural fine tuning. Our method first identifies category specific keywords using TF-IDF analysis on 35,522 training samples. These keywords then inform prompt engineering for Llama 3.1 8B model fine tuned with LoRA adapters. We incorporate distinctive Bangla terms directly into classification prompts to guide the model understanding of hate speech patterns. Our system achieved top 5 rankings across all three BLP 2025 Task 1 subtasks including hate type classification, target identification, and multi task prediction. The approach proved particularly effective for culturally specific hate speech patterns unique to Bangla social media discourse.