%0 Conference Proceedings %T Better Transition-Based AMR Parsing with a Refined Search Space %A Guo, Zhijiang %A Lu, Wei %Y Riloff, Ellen %Y Chiang, David %Y Hockenmaier, Julia %Y Tsujii, Jun’ichi %S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing %D 2018 %8 oct nov %I Association for Computational Linguistics %C Brussels, Belgium %F guo-lu-2018-better %X This paper introduces a simple yet effective transition-based system for Abstract Meaning Representation (AMR) parsing. We argue that a well-defined search space involved in a transition system is crucial for building an effective parser. We propose to conduct the search in a refined search space based on a new compact AMR graph and an improved oracle. Our end-to-end parser achieves the state-of-the-art performance on various datasets with minimal additional information. %R 10.18653/v1/D18-1198 %U https://aclanthology.org/D18-1198 %U https://doi.org/10.18653/v1/D18-1198 %P 1712-1722