Sanjida Islam Era, Tanjeem Azwad Zaman, Ishika Tarin Ime, Ashequl Islam Tofa, Farzin Awsaf Zaman, A. B. M. Alim al Islam
Examining whether technical accessibility measures align with users' usability experiences across web systems in Bangladesh is of utmost significance, given the country's rapidly expanding digital ecosystem. We, therefore, evaluated 207 web systems using WAVE, AChecks, and Squidler and surveyed 103 young adult users. Automated evaluation revealed mixed patterns across government and non-government sectors, with neither consistently performing better across accessibility, functionality, web practices, and performance. In contrast, participants clearly favored non-government platforms for navigation, usability, clarity, and cross-device experience. Qualitative analysis identified three barriers to practical access: system instability disrupting task completion, interaction and navigational friction, and socio-technical barriers involving information access, service continuity, and trust. These findings show that automated accessibility measures capture only part of users' experiences. Accessible web services therefore require attention beyond detectable accessibility problems to reliable task progression, understandable interaction, information access, and continuity across the broader service process.
Kazi Reyazul Hasan Bangladesh University of Engineering and Technology, A. B. M. Alim Al Islam Bangladesh University of Engineering and Technology, Muhammad Abdullah Adnan Bangladesh University of Engineering and Technology
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.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表