This paper presents IIMAS-RAG, our system for SemEval-2026 Task 8 on evaluating multi-turn retrieval-augmented generation. Our approach combines LLM-based query rewriting, hybrid sparse-dense retrieval with SPLADE and Voyage-3-large fused via Reciprocal Rank Fusion, and answerability-conditioned generation with GPT-4.1. The system ranked 4th out of 38 teams in Subtask A (Retrieval) and 13th out of 29 teams in Subtask C (Full RAG). Our results show that query rewriting is the most impactful retrieval component, while generation remains challenging in low-context and partially answerable scenarios.

This paper addresses our approach to Task 11 (Track A and B) at the SemEval-2025, which focuses on the challenge of multilingual emotion detection in text, specifically identifying perceived emotions. The task is divided into tracks, we participated in two tracks: Track A, involving multilabel emotion detection, and Track B, which extends this to predicting emotion intensity on an ordinal scale. Addressing the challenges of imbalanced data and linguistic diversity, we propose a robust approach using pre-trained language models, fine-tuned with techniques such as extensive and deep hyperparameter optimization, along with loss function combinations to improve performance on imbalanced datasets and underrepresented languages. Our results demonstrate strong performance on Track A, particularly in low-resource languages such as Tigrinya (ranked 2nd), Igbo (ranked 3rd), and Oromo (ranked 4th). This work offers a scalable framework for emotion detection with applications in cross-cultural communication and human-computer interaction.

This paper addresses the shared task of multi-lingual emotion detection in tweets, presented at the Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis (WASSA) co-located with the ACL 2024 conference. The task involves predicting emotions from six classes in tweets from five different languages using only English for model training. Our approach focuses on addressing class imbalance through data augmentation, hierarchical classification, and the application of focal loss and weighted cross-entropy loss functions. These methods enhance our transformer-based model’s ability to transfer emotion detection capabilities across languages, resulting in improved performance despite the constraints of limited computational resources.