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Dependency or Span, End-to-End Uniform Semantic Role Labeling

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arxiv 1901.05280 v1 pith:ZC5OAE7Y submitted 2019-01-16 cs.CL

Zuchao Li , Shexia He , Hai Zhao , Yiqing Zhang , Zhuosheng Zhang , Xi Zhou , Xiang Zhou This is my paper · ORCID
classification cs.CL
keywords dependencyend-to-endmodelsemanticspanargumentconlllabeling
verification ladder T0 review T1 audit T2 compute T3 formal
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Semantic role labeling (SRL) aims to discover the predicateargument structure of a sentence. End-to-end SRL without syntactic input has received great attention. However, most of them focus on either span-based or dependency-based semantic representation form and only show specific model optimization respectively. Meanwhile, handling these two SRL tasks uniformly was less successful. This paper presents an end-to-end model for both dependency and span SRL with a unified argument representation to deal with two different types of argument annotations in a uniform fashion. Furthermore, we jointly predict all predicates and arguments, especially including long-term ignored predicate identification subtask. Our single model achieves new state-of-the-art results on both span (CoNLL 2005, 2012) and dependency (CoNLL 2008, 2009) SRL benchmarks.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Semantics-aware BERT for Language Understanding

    cs.CL 2019-09 conditional novelty 6.0 of 10

    Feeding semantic role labels into BERT alongside the text improves performance on ten NLU benchmarks over the BERT baseline.

  2. Syntax-aware Multilingual Semantic Role Labeling

    cs.CL 2019-09 conditional novelty 5.0 of 10

    A syntax-guided argument pruning method plus multilingual BERT embeddings sets new state-of-the-art semantic role labeling results on all seven CoNLL-2009 languages.

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