Idiom Processing

Idiom processing in natural language processing (NLP) focuses on enabling computers to understand and correctly utilize idiomatic expressions, whose meanings differ from the literal meanings of their constituent words. Current research emphasizes improving the representation of idioms within large language models (LLMs) using techniques like contrastive learning, adapter modules, and retrieval augmentation, often incorporating contextual information from surrounding text or external knowledge bases. These advancements aim to enhance various NLP tasks, including machine translation, sentiment analysis, and conversational AI, by improving the accuracy and fluency of language processing systems.