# Non-Monotonic Sequential Text Generation

arXiv: https://arxiv.org/abs/1902.02192 · HTML: https://mlsanity.com/paper/1902.02192/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2019.

## Built on by (most cited first, 30 of 55)

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- [InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model](https://mlsanity.com/paper/2102.11008/) (2021)
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- [PathFinder: Guided Search over Multi-Step Reasoning Paths](https://mlsanity.com/paper/2312.05180/) (2023)
- [The Belief State Transformer](https://mlsanity.com/paper/2410.23506/) (2024)
- [Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions](https://mlsanity.com/paper/2505.05755/) (2025)
- [Exploring Dynamic Selection of Branch Expansion Orders for Code Generation](https://mlsanity.com/paper/2106.00261/) (2021)
- [Retrieval is Accurate Generation](https://mlsanity.com/paper/2402.17532/) (2024)
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- [Linguistically inspired roadmap for building biologically reliable protein language models](https://mlsanity.com/paper/2207.00982/) (2022)
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## Abstract (arXiv)

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. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order, while achieving competitive performance with conventional left-to-right generation.