NBF at SemEval-2025 Task 5: Light-Burst Attention Enhanced System for Multilingual Subject Recommendation

We present our system submission for SemEval 2025 Task 5, which focuses on cross-lingual subject classification in the English and German academic domains. Our approach leverages bilingual data during training, employing negative sampling and a margin-based retrieval objective. We demonstrate that a dimension-as-token self-attention mechanism designed with significantly reduced internal dimensions can effectively encode sentence embeddings for subject retrieval. In quantitative evaluation, our system achieved an average recall rate of 32.24% in the general quantitative setting (all subjects), 43.16% and 31.53% of the general qualitative evaluation methods with minimal GPU usage, highlighting their competitive performance. Our results demonstrate that our approach is effective in capturing relevant subject information under resource constraints, although there is still room for improvement.

Bengali to Assamese Statistical Machine Translation using Moses (Corpus Based)

Machine dialect interpretation assumes a real part in encouraging man-machine correspondence and in addition men-men correspondence in Natural Language Processing (NLP). Machine Translation (MT) alludes to utilizing machine to change one dialect to an alternate. Statistical Machine Translation is a type of MT consisting of Language Model (LM), Translation Model (TM) and decoder. In this paper, Bengali to Assamese Statistical Machine Translation Model has been created by utilizing Moses. Other translation tools like IRSTLM for Language Model and GIZA-PP-V1.0.7 for Translation model are utilized within this framework which is accessible in Linux situations. The purpose of the LM is to encourage fluent output and the purpose of TM is to encourage similarity between input and output, the decoder increases the probability of translated text in target language. A parallel corpus of 17100 sentences in Bengali and Assamese has been utilized for preparing within this framework. Measurable MT procedures have not so far been generally investigated for Indian dialects. It might be intriguing to discover to what degree these models can help the immense continuous MT deliberations in the nation.