Multilingual Track

Multilingual track research focuses on developing and evaluating natural language processing (NLP) models that can effectively handle multiple languages simultaneously. Current efforts concentrate on tasks like machine-generated text detection, named entity recognition, and speech translation, often leveraging large language models (LLMs) and parameter-efficient multilingual architectures to improve performance, particularly in low-resource language settings. This research is significant because it advances the capabilities of NLP systems to process and understand a wider range of languages, impacting applications such as cross-lingual information retrieval, multilingual content moderation, and improved access to information for diverse communities.