Non-Monotonic Sequential Text Generation

Understand

Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right.

• In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation.
• Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree.
• Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy's own preferences.