AMR Parsing via Graph-Sequence Iterative Inference
This work addresses the problem of improving parsing accuracy for Abstract Meaning Representation (AMR), which is incremental but shows strong gains over prior methods.
The paper tackles AMR parsing by proposing an end-to-end model that iteratively infers decisions on input sequences and output graphs, achieving state-of-the-art results with 80.2% on AMR 2.0 and 75.4% on AMR 1.0 datasets.
We propose a new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph. At each time step, our model performs multiple rounds of attention, reasoning, and composition that aim to answer two critical questions: (1) which part of the input \textit{sequence} to abstract; and (2) where in the output \textit{graph} to construct the new concept. We show that the answers to these two questions are mutually causalities. We design a model based on iterative inference that helps achieve better answers in both perspectives, leading to greatly improved parsing accuracy. Our experimental results significantly outperform all previously reported \textsc{Smatch} scores by large margins. Remarkably, without the help of any large-scale pre-trained language model (e.g., BERT), our model already surpasses previous state-of-the-art using BERT. With the help of BERT, we can push the state-of-the-art results to 80.2\% on LDC2017T10 (AMR 2.0) and 75.4\% on LDC2014T12 (AMR 1.0).