Sequence-to-sequence AMR Parsing with Ancestor Information

Abstract

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.

Topics & Concepts