Emotion detection in multilingual settings presents significant challenges, particularly for low-resource languages where labeled datasets are scarce. To address these limitations, we introduce EmoRationale, a Retrieval-Augmented Generation (RAG) framework designed to enhance explainability and cross-lingual generalization in emotion detection. Our approach combines vector-based retrieval with in-context learning in large language models (LLMs), using semantically relevant examples to enhance classification accuracy and interpretability. Unlike traditional fine-tuning methods, our system provides evidence-based reasoning for its predictions, making emotion detection more transparent and adaptable across diverse linguistic contexts. Experimental results on the SemEval-2025 Task 11 dataset demonstrate that our RAG-based method achieves strong performance in multi-label emotion classification, emotion intensity assessment, and cross-lingual emotion transfer, surpassing conventional models in interpretability while remaining cost-effective.

This paper presents a combination of data augmentation methods to boost the performance of state-of-the-art transformer-based language models for Patronizing and Condescending Language (PCL) detection and multi-label PCL classification tasks. These tasks are inherently different from sentiment analysis because positive/negative hidden attitudes in the context will not necessarily be considered positive/negative for PCL tasks. The oblation study observes that the imbalance degree of PCL dataset is in the extreme range. This paper presents a modified version of the sentence paraphrasing deep learning model (PEGASUS) to tackle the limitation of maximum sequence length. The proposed algorithm has no specific maximum input length to paraphrase sequences. Our augmented underrepresented class of annotated data achieved competitive results among top-16 SemEval-2022 participants. This paper’s approaches rely on fine-tuning pretrained RoBERTa and GPT3 models such as Davinci and Curie engines with an extra-enriched PCL dataset. Furthermore, we discuss Few-Shot learning technique to overcome the limitation of low-resource NLP problems.

In this paper, we investigate a commonsense inference task that unifies natural language understanding and commonsense reasoning. We describe our attempt at SemEval-2020 Task 4 competition: Commonsense Validation and Explanation (ComVE) challenge. We discuss several state-of-the-art deep learning architectures for this challenge. Our system uses prepared labeled textual datasets that were manually curated for three different natural language inference subtasks. The goal of the first subtask is to test whether a model can distinguish between natural language statements that make sense and those that do not make sense. We compare the performance of several language models and fine-tuned classifiers. Then, we propose a method inspired by question/answering tasks to treat a classification problem as a multiple choice question task to boost the performance of our experimental results (96.06%), which is significantly better than the baseline. For the second subtask, which is to select the reason why a statement does not make sense, we stand within the first six teams (93.7%) among 27 participants with very competitive results. Our result for last subtask of generating reason against the nonsense statement shows many potentials for future researches as we applied the most powerful generative model of language (GPT-2) with 6.1732 BLEU score among first four teams. .