Word Ordering Without Syntax
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Apr 2016.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 1; 2024: 0; 2025: 1.
7 papers.
- 2023Amortizing intractable inference in large language modelscited by 94
- 2021Investigating Failures of Automatic Translation in the Case of Unambiguous Gendercited by 20
- 2021Studying word order through iterative shufflingcited by 9
- 2021Unlabeled Principal Component Analysis and Matrix Completioncited by 5
- 2022On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BARTcited by 2
- 2021Classification Models for Partially Ordered Sequencescited by 0
- 2025Similarity-Distance-Magnitude Universal Verificationcited by 0
- 2025Similarity-Distance-Magnitude Universal Verification27 Feb 2025
- 2023Amortizing intractable inference in large language models6 Oct 2023
- 2022On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART15 Apr 2022
- 2021Studying word order through iterative shuffling10 Sep 2021
- 2021Investigating Failures of Automatic Translation in the Case of Unambiguous Gender16 Apr 2021
- 2021Classification Models for Partially Ordered Sequences31 Jan 2021
- 2021Unlabeled Principal Component Analysis and Matrix Completion23 Jan 2021