Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards
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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.
Original language | English |
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Title of host publication | Proceedings of the AAAI-21 International Joint Conference on Artificial Intelligence |
Number of pages | 10 |
Publisher | AAAI Press |
Publication date | 2021 |
ISBN (Electronic) | 978-1-57735-866-4 |
Publication status | Published - 2021 |
Event | 35th AAAI Conference on Artificial Intelligence - Virtual Duration: 2 Feb 2021 → 9 Feb 2021 |
Conference
Conference | 35th AAAI Conference on Artificial Intelligence |
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By | Virtual |
Periode | 02/02/2021 → 09/02/2021 |
Series | Proceedings of the International Joint Conference on Artificial Intelligence |
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Number | 14 |
Volume | 35 |
ISSN | 1045-0823 |
ID: 287187332