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Sequence tagging is the basis for multiple applications in natural language processing. Despite successes in learning long term token sequence dependencies with neural network, tag dependencies are rarely considered previously. Sequence tagging actually possesses complex dependencies and interactions among the input tokens and the output tags. We propose a novel multi-channel model, which handles different ranges of token-tag dependencies and their interactions simultaneously. A tag LSTM isdoi:10.24963/ijcai.2018/637 dblp:conf/ijcai/ZhangCZLY18 fatcat:tpzgokv5ereavio4u57tftsc2i