Abstract Meaning Representation to Text

Abstract Meaning Representation (AMR) to text generation focuses on converting AMR graphs, which represent the semantic meaning of sentences, into natural language text. Current research emphasizes improving the accuracy and efficiency of this conversion, exploring techniques like transition-based parsing with sliding windows, incorporating structural information via graph neural networks and relative positional embeddings, and leveraging pre-trained language models. This area is significant because it advances natural language generation, enabling applications such as improved machine translation, text summarization, and question answering systems that offer greater interpretability and accuracy.