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A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling
[article]
2019
arXiv
pre-print
Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. Inspired by the strong correlation between syntax and semantics, previous works pay much attention to improve SRL performance on exploiting syntactic knowledge, achieving significant results. Pipeline methods based on automatic syntactic trees and multi-task learning (MTL) approaches using standard syntactic trees are two common research orientations. In this paper, we adopt a simple unified span-based
arXiv:1911.04641v1
fatcat:t3ak2fwywnb63gifuvuspzj4vi