MESSAI.iO
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Abstract

We propose a novel semantic tagging task, sem-tagging, tailored for the purpose of multilingual semantic parsing, and present the first tagger using deep residual networks (ResNets). Our tagger uses both word and character representations and includes a novel residual bypass architecture. We evaluate the tagset both intrinsically on the new task of semantic tagging, as well as on Part-of-Speech (POS) tagging. Our system, consisting of a ResNet and an auxiliary loss function predicting our semantic tags, significantly outperforms prior results on English Universal Dependencies POS tagging (95.71% accuracy on UD v1.2 and 95.67% accuracy on UD v1.3).

Keywords

Artificial intelligenceNatural language processingResidualParsingTask (project management)Residual neural network

Identifiers

DOI
10.48550/arxiv.1609.07053
Journal
arXiv (Cornell University)
Year
2016