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We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-ofthe-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-based encoding.doi:10.18653/v1/e17-2118 dblp:conf/eacl/BethardMDLS17 fatcat:vobk2vw5yndqbjjq2t73ywsp2a