Abstract

In this paper, we investigate the aspect of structured output modeling for the state-of-the-art graph-based neural dependency parser (Dozat and Manning, 2017). With evaluations on 14 treebanks, we empirically show that global output-structured models can generally obtain better performance, especially on the metric of sentence-level Complete Match. However, probably because neural models already learn good global views of the inputs, the improvement brought by structured output modeling is modest.

Date

January 1, 1970

Authors

Zhisong Zhang, Xuezhe Ma, Eduard Hovy

Conference

Proceedings of the 57th Conference of the Association for Computational Linguistics (ACL-2019)

Pages

5592-5598