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AbstractSequence-to-Sequence (S2S) models have achieved remarkable success on various text generation tasks.However, learning complex structures with S2S models remains challenging as external neural modules and additional lexicons are often supplemented to predict non-textual outputs.We present a systematic study of S2S modeling using contained de
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