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Coreference resolution and semantic role labeling are NLP tasks that capturedifferent aspects of semantics, indicating respectively, which expressionsrefer to the same entity, and what semantic roles expressions serve in thesentence. However, they are often closely interdependent, and both generallynecessitate natural language understanding. Do they form a coherent abstractrepresentation of documents? We present a neural network architecture for jointcoreference resolution and semantic role labeling for English, and train graphneural networks to model the 'coherence' of the combined shallow semanticgraph. Using the resulting coherence score as a reward for our joint semanticanalyzer, we use reinforcement learning to encourage global coherence over thedocument and between semantic annotations. This leads to improvements on bothtasks in multiple datasets from different domains, and across a range ofencoders of different expressivity, calling, we believe, for a more holisticapproach to semantics in NLP.

Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards