AMR Parsing with an Incremental Joint Model

Junsheng Zhou1, Feiyu Xu2, Hans Uszkoreit3, Weiguang QU1, Ran Li1, Yanhui Gu1
1Nanjing Normal University, 2DFKI LT Lab, 3DFKI and Saarland University


Abstract

To alleviate the error propagation in the traditional pipelined models for Abstract Meaning Representation (AMR) parsing, we formulate AMR parsing as a joint task that performs the two subtasks: concept identification and relation identification simultaneously. To this end, we first develop a novel component-wise beam search algorithm for relation identification in an incremental fashion, and then in-corporate the decoder into a unified frame-work based on multiple-beam search, which allows for the bi-directional information flow between the two subtasks in a single incremental model. Experiments on the public datasets demonstrate that our joint model significantly outperforms the previous pipelined counterparts, and also achieves better or comparable performance than other approaches to AMR parsing, without utilizing external semantic resources.