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Joint extraction from unstructured text aims to extract relational triples composed of entity pairs and their relations. However, most existing works fail to process the overlapping issues that occur when the same entities are utilized to generate different relational triples in a sentence. In this work, we propose a mutually exclusive Binary Cross Tagging (BCT) scheme and develop the end-to-end BCT framework to jointly extract overlapping entities and triples. Each token of entities is assigned a mutually exclusive binary tag, and then these tags are cross-matched in all tag sequences to form triples. Our method is compared with other state-of-the-art models in two English public datasets and a large-scale Chinese dataset. Experiments show that our proposed framework achieves encouraging performance in F1 scores for the three datasets investigated. Further detailed analysis demonstrates that our method achieves strong performance overall with three overlapping patterns, especially when the overlapping problem becomes complex.

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Reinforcement learning in a multi-agent setting is very important for real-world applications, but it brings more challenges than those in a single-agent environment. In the multi-agent setting, the agent generally has a bias of overestimation on the value function. In our work, we pay attention to the issue of overestimation bias with continuous actions in the multi-agent learning environment. We propose a method to reduce this bias by adopting the distributional perspective on reinforcement learning. We combine it within the framework of off-policy learning Actor-Critic and propose a novel approach Multi-Agent Deep Distributional Deterministic Policy Gradient (MAD3PG). We empirically evaluate it in three competitive and cooperative multi-agent settings. Our results show that in a series of difficult motor tasks the agents trained by MAD3PG significantly outperforms existing benchmark.