# Word Ordering Without Syntax

arXiv: https://arxiv.org/abs/1604.08633 · HTML: https://mlsanity.com/paper/1604.08633/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2016.

## Built on by (most cited first, 7 of 7)

- [Amortizing intractable inference in large language models](https://mlsanity.com/paper/2310.04363/) (2023)
- [Investigating Failures of Automatic Translation in the Case of Unambiguous Gender](https://mlsanity.com/paper/2104.07838/) (2021)
- [Studying word order through iterative shuffling](https://mlsanity.com/paper/2109.04867/) (2021)
- [Unlabeled Principal Component Analysis and Matrix Completion](https://mlsanity.com/paper/2101.09446/) (2021)
- [On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART](https://mlsanity.com/paper/2204.07367/) (2022)
- [Classification Models for Partially Ordered Sequences](https://mlsanity.com/paper/2102.00380/) (2021)
- [Similarity-Distance-Magnitude Universal Verification](https://mlsanity.com/paper/2502.20167/) (2025)

## Abstract (arXiv)

Recent work on word ordering has argued that syntactic structure is important, or even required, for effectively recovering the order of a sentence. We find that, in fact, an n-gram language model with a simple heuristic gives strong results on this task. Furthermore, we show that a long short-term memory (LSTM) language model is even more effective at recovering order, with our basic model outperforming a state-of-the-art syntactic model by 11.5 BLEU points. Additional data and larger beams yield further gains, at the expense of training and search time.