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Sequence-to-Sequence (S2S) models have achieved remarkable success on varioustext generation tasks. However, learning complex structures with S2S modelsremains challenging as external neural modules and additional lexicons areoften supplemented to predict non-textual outputs. We present a systematicstudy of S2S modeling using contained decoding on four core tasks:part-of-speech tagging, named entity recognition, constituency and dependencyparsing, to develop efficient exploitation methods costing zero extraparameters. In particular, 3 lexically diverse linearization schemas andcorresponding constrained decoding methods are designed and evaluated.Experiments show that although more lexicalized schemas yield longer outputsequences that require heavier training, their sequences being closer tonatural language makes them easier to learn. Moreover, S2S models using ourconstrained decoding outperform other S2S approaches using external resources.Our best models perform better than or comparably to the state-of-the-art forall 4 tasks, lighting a promise for S2S models to generate non-sequentialstructures.

Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing