AMR Parsing with Cache Transition Systems

Abstract

In this paper, we present a transition system that generalizes transition-based dependency parsing techniques to generateAMR graphs rather than tree structures. In addition to a buffer and a stack, we use a fixed-size cache, and allow the system to build arcs to any vertices present in the cache at the same time. The size of the cache provides a parameter that can trade off between the complexity of the graphs that can be built and the ease of predicting actions during parsing. Our results show that a cache transition system can cover almost all AMR graphs with a small cache size, and our end-to-end system achieves competitive results in comparison with other transition-based approaches for AMR parsing.

Cite

Text

Peng et al. "AMR Parsing with Cache Transition Systems." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.11922

Markdown

[Peng et al. "AMR Parsing with Cache Transition Systems." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/peng2018aaai-amr/) doi:10.1609/AAAI.V32I1.11922

BibTeX

@inproceedings{peng2018aaai-amr,
  title     = {{AMR Parsing with Cache Transition Systems}},
  author    = {Peng, Xiaochang and Gildea, Daniel and Satta, Giorgio},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {4897-4904},
  doi       = {10.1609/AAAI.V32I1.11922},
  url       = {https://mlanthology.org/aaai/2018/peng2018aaai-amr/}
}