Sequence-to-sequence AMR Parsing with Ancestor Information
Yanjing Chen, Daniel Gildea · 2022
AMR parsing is the task of mapping a sentence to an AMR semantic graph automatically.The difficulty comes from generating the complex graph structure.The previous state-of-the-art method translates the AMR graph into a sequence, then directly fine-tunes a pretrained sequence-to-sequence Transformer model (BART).However, purely treating the graph as a sequence does not take advantage of structural information about the graph.In this paper, we design several strategies to add the important ancestor information into the Transformer Decoder.Our experiments 1 show that we can improve the performance for both the AMR 2.0 and AMR 3.0 dataset and achieve new state-of-the-art results.