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- W3100841630 abstract "Semantic role labeling is primarily used to identify predicates, arguments, and their semantic relationships. Due to the limitations of modeling methods and the conditions of pre-identified predicates, previous work has focused on the relationships between predicates and arguments and the correlations between arguments at most, while the correlations between predicates have been neglected for a long time. High-order features and structure learning were very common in modeling such correlations before the neural network era. In this paper, we introduce a high-order graph structure for the neural semantic role labeling model, which enables the model to explicitly consider not only the isolated predicate-argument pairs but also the interaction between the predicate-argument pairs. Experimental results on 7 languages of the CoNLL-2009 benchmark show that the high-order structural learning techniques are beneficial to the strong performing SRL models and further boost our baseline to achieve new state-of-the-art results." @default.
- W3100841630 created "2020-11-23" @default.
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- W3100841630 date "2020-01-01" @default.
- W3100841630 modified "2023-09-24" @default.
- W3100841630 title "High-order Semantic Role Labeling" @default.
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- W3100841630 doi "https://doi.org/10.18653/v1/2020.findings-emnlp.102" @default.
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