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• W2900064420 abstract "Do unsupervised methods for learning rich, contextualized token representations obviate the need for explicit modeling of linguistic structure in neural network models for semantic role labeling (SRL)? We address this question by incorporating the massively successful ELMo embeddings (Peters et al., 2018) into LISA (Strubell and McCallum, 2018), a strong, linguistically-informed neural network architecture for SRL. In experiments on the CoNLL-2005 shared task we find that though ELMo out-performs typical word embeddings, beginning to close the gap in F1 between LISA with predicted and gold syntactic parses, syntactically-informed models still out-perform syntax-free models when both use ELMo, especially on out-of-domain data. Our results suggest that linguistic structures are indeed still relevant in this golden age of deep learning for NLP." @default.
• W2900064420 created "2018-11-16" @default.
• W2900064420 creator A5008354502 @default.
• W2900064420 creator A5051203366 @default.
• W2900064420 date "2018-01-01" @default.
• W2900064420 modified "2023-09-25" @default.
• W2900064420 title "Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL?" @default.
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• W2900064420 doi "https://doi.org/10.18653/v1/w18-2904" @default.
• W2900064420 hasPublicationYear "2018" @default.
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