Neural Semantic

Neural semantic parsing aims to automatically translate natural language into formal, machine-readable representations, enabling computers to understand and reason with human language. Current research focuses on improving the accuracy and robustness of these parsers, particularly for complex sentences and out-of-domain queries, often employing sequence-to-sequence models, graph neural networks, and large language models fine-tuned for specific tasks. These advancements are crucial for building more sophisticated natural language interfaces and question-answering systems across diverse applications, including knowledge-based systems and clinical information retrieval. Ongoing efforts also address challenges like hallucination, compositional generalization, and efficient training with limited data.