This paper describes the system developed by the HITSZ-HLT team for WASSA-2024 Shared Task 2, which addresses two closely linked sub-tasks: Cross-lingual Emotion Detection and Binary Trigger Word Detection in tweets. The main goal of Shared Task 2 is to simultaneously identify the emotions expressed and detect the trigger words across multiple languages. To achieve this, we introduce a Language-agnostic Multi Task Learning (LaMTL) framework that integrates emotion prediction and emotion trigger word detection tasks. By fostering synergistic interactions between task-specific and task-agnostic representations, the LaMTL aims to mutually enhance emotional cues, ultimately improving the performance of both tasks. Additionally, we leverage large-scale language models to translate the training dataset into multiple languages, thereby fostering the formation of language-agnostic representations within the model, significantly enhancing the model’s ability to transfer and perform well across multilingual data. Experimental results demonstrate the effectiveness of our framework across both tasks, with a particular highlight on its success in achieving second place in sub-task 2.

SemEval-2024 Task 8 introduces the challenge of identifying machine-generated texts from diverse Large Language Models (LLMs) in various languages and domains. The task comprises three subtasks: binary classification in monolingual and multilingual (Subtask A), multi-class classification (Subtask B), and mixed text detection (Subtask C). This paper focuses on Subtask A & B. To tackle this task, this paper proposes two methods: 1) using traditional machine learning (ML) with natural language preprocessing (NLP) for feature extraction, and 2) fine-tuning LLMs for text classification. For fine-tuning, we use the train datasets provided by the task organizers. The results show that transformer models like LoRA-RoBERTa and XLM-RoBERTa outperform traditional ML models, particularly in multilingual subtasks. However, traditional ML models performed better than transformer models for the monolingual task, demonstrating the importance of considering the specific characteristics of each subtask when selecting an appropriate approach.