An empirical investigation of structured output modeling for graph-based neural dependency parsing
Proceedings of the 57th Annual Meeting of the Association for …, 2019•aclanthology.org
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.
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.
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.
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