Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments

• URL: http://arxiv.org/abs/2110.06865v1
• Date: Wed, 13 Oct 2021 17:02:29 GMT
• Title: Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
• Authors: Yu Zhang, Qingrong Xia, Shilin Zhou, Yong Jiang, Zhenghua Li, Guohong Fu, Min Zhang
• Abstract summary: Recent works of SRL mainly fall into two lines:1) BIO-based and 2) span-based. We propose to reduce SRL to a dependency parsing task and regard the flat argument spans as latent subtrees. In particular, we equip our formulation with a novel span-constrained TreeCRF model to make tree structures span-aware.
• Score: 33.95412952615206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic role labeling is a fundamental yet challenging task in the NLP community. Recent works of SRL mainly fall into two lines:1) BIO-based and 2) span-based. Despite effectiveness, they share some intrinsic drawbacks of not explicitly considering internal argument structures, which may potentially hinder the model's expressiveness. To remedy this, we propose to reduce SRL to a dependency parsing task and regard the flat argument spans as latent subtrees. In particular, we equip our formulation with a novel span-constrained TreeCRF model to make tree structures span-aware, and further extend it to the second-order case. Experiments on CoNLL05 and CoNLL12 benchmarks reveal that the results of our methods outperform all previous works and achieve the state-of-the-art.

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