mlsanityData to 14 Oct 2026

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

arXiv 2110.06865 ↗First posted 13 Oct 2021cs.CLcited by 14

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Abstract (arXiv)

Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community. Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite ubiquity, they share some intrinsic drawbacks of not considering internal argument structures, potentially hindering the model's expressiveness. The key challenge is arguments are flat structures, and there are no determined subtree realizations for words inside arguments. To remedy this, in this paper, we propose to regard flat argument spans as latent subtrees, accordingly reducing SRL to a tree parsing task. In particular, we equip our formulation with a novel span-constrained TreeCRF to make tree structures span-aware and further extend it to the second-order case. We conduct extensive experiments on CoNLL05 and CoNLL12 benchmarks. Results reveal that our methods perform favorably better than all previous syntax-agnostic works, achieving new state-of-the-art under both end-to-end and w/ gold predicates settings.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 50 of its 126 references that are in the map. First posted on arXiv 13 Oct 2021.

Builds on

50 of 126 references.

References · 50

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Citing papers per year: 2021: 1; 2022: 4; 2023: 5; 2024: 3; 2025: 1.

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