DFKI-LT - AMR Parsing with an Incremental Joint Model
AMR Parsing with an Incremental Joint Model
3 Proceedings of EMNLP 2016, Austin, Texas, USA, Springer, 2016
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 incorporate the decoder into a unified framework 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 performance than other approaches to AMR parsing, without utilizing external semantic resources.