AMR Parsing With Cache Transition Systems

Authors

  • Xiaochang Peng University of Rochester
  • Daniel Gildea University of Rochester
  • Giorgio Satta University of Padua

Keywords:

AMR parsing, cache transition system, semantic parsing

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.

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Published

2018-04-26

How to Cite

Peng, X., Gildea, D., & Satta, G. (2018). AMR Parsing With Cache Transition Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11922

Issue

Section

Main Track: NLP and Knowledge Representation