Standard neural sequence generation methods assume a pre-specified generation order, such as left-to-right generation. Despite its wild success in recent years, there’s a lingering question of whether this is necessary and if there is any other way to generate such a sequence in an order automatically learned from data without having to pre-specify it or relying on external tools. I will discuss in this talk three alternatives; parallel decoding, recursive set prediction, and insertion-based generation.

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