TY - GEN

T1 - Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards

AU - Aralikatte, Rahul Rajendra

AU - Abdou, Mostafa Hany Mohamed Anwar

AU - Lent, Heather Christine

AU - Hershcovich, Daniel

AU - Søgaard, Anders

PY - 2021

Y1 - 2021

N2 - Coreference resolution and semantic role labeling are NLP tasks that capture different aspects of semantics, indicating respectively, which expressions refer to the same entity, and what semantic roles expressions serve in the sentence. However, they are often closely interdependent, and both generally necessitate natural language understanding. Do they form a coherent abstract representation of documents? We present a neural network architecture for joint coreference resolution and semantic role labeling for English, and train graph neural networks to model the 'coherence' of the combined shallow semantic graph. Using the resulting coherence score as a reward for our joint semantic analyzer, we use reinforcement learning to encourage global coherence over the document and between semantic annotations. This leads to improvements on both tasks in multiple datasets from different domains, and across a range of encoders of different expressivity, calling, we believe, for a more holistic approach to semantics in NLP.

AB - Coreference resolution and semantic role labeling are NLP tasks that capture different aspects of semantics, indicating respectively, which expressions refer to the same entity, and what semantic roles expressions serve in the sentence. However, they are often closely interdependent, and both generally necessitate natural language understanding. Do they form a coherent abstract representation of documents? We present a neural network architecture for joint coreference resolution and semantic role labeling for English, and train graph neural networks to model the 'coherence' of the combined shallow semantic graph. Using the resulting coherence score as a reward for our joint semantic analyzer, we use reinforcement learning to encourage global coherence over the document and between semantic annotations. This leads to improvements on both tasks in multiple datasets from different domains, and across a range of encoders of different expressivity, calling, we believe, for a more holistic approach to semantics in NLP.

M3 - Article in proceedings

T3 - Proceedings of the International Joint Conference on Artificial Intelligence

BT - Proceedings of the AAAI-21 International Joint Conference on Artificial Intelligence

PB - AAAI Press

Y2 - 2 February 2021 through 9 February 2021

ER -