Multilingual Text Classification

Multilingual text classification aims to automatically categorize text from multiple languages, a crucial task with broad applications. Current research focuses on improving model accuracy and efficiency using techniques like parameter-efficient fine-tuning (e.g., adapters and LoRA), adversarial training to enhance robustness, and contrastive learning to mitigate bias. These advancements leverage various deep learning architectures, including transformer-based models and graph neural networks, often incorporating multilingual embeddings and pre-trained language models. The field's progress significantly impacts diverse areas, from cross-lingual information retrieval and social media monitoring to building fairer and more inclusive NLP systems.