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Extracting temporal relationships between pairs of events in texts is acrucial yet challenging problem for natural language understanding. Dependingon the distance between the events, models must learn to differently balanceinformation from local and global contexts surrounding the event pair fortemporal relation prediction. Learning how to fuse this information has provedchallenging for transformer-based language models. Therefore, we present MulCo:Multi-Scale Contrastive Co-Training, a technique for the better fusion of localand global contextualized features. Our model uses a BERT-based language modelto encode local context and a Graph Neural Network (GNN) to represent globaldocument-level syntactic and temporal characteristics. Unlike previousstate-of-the-art methods, which use simple concatenation on multi-view featuresor select optimal sentences using sophisticated reinforcement learningapproaches, our model co-trains GNN and BERT modules using a multi-scalecontrastive learning objective. The GNN and BERT modules learn a synergisticparameterization by contrasting GNN multi-layer multi-hop subgraphs (i.e.,global context embeddings) and BERT outputs (i.e., local context embeddings)through end-to-end back-propagation. We empirically demonstrate that MulCoprovides improved ability to fuse local and global contexts encoded using BERTand GNN compared to the current state-of-the-art. Our experimental results showthat MulCo achieves new state-of-the-art results on several temporal relationextraction datasets.

Multi-Scale Contrastive Co-Training for Event Temporal Relation Extraction