mlsanityData to 7 Oct 2026

AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation

arXiv 2211.08387 ↗First posted 15 Nov 2022cs.CLcited by 6

Not on the map. Why?

Abstract (arXiv)

Lexically constrained text generation is one of the constrained text generation tasks, which aims to generate text that covers all the given constraint lexicons. While the existing approaches tackle this problem using a lexically constrained beam search algorithm or dedicated model using non-autoregressive decoding, there is a trade-off between the generated text quality and the hard constraint satisfaction. We introduce AutoTemplate, a simple yet effective lexically constrained text generation framework divided into template generation and lexicalization tasks. The template generation is to generate the text with the placeholders, and lexicalization replaces them into the constraint lexicons to perform lexically constrained text generation. We conducted the experiments on two tasks: keywords-to-sentence generations and entity-guided summarization. Experimental results show that the AutoTemplate outperforms the competitive baselines on both tasks while satisfying the hard lexical constraints. The code is available at https://github.com/megagonlabs/autotemplate

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 39 of its 68 references that are in the map. First posted on arXiv 15 Nov 2022.

Builds on

39 of 68 references.

References · 39

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  6. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  7. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  8. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  9. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  10. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  11. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  12. 2020GLU Variants Improve Transformercited by 1,724
27 more
  1. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  2. 2015Teaching Machines to Read and Comprehendcited by 1,039
  3. 2018Neural Network Acceptability Judgmentscited by 952
  4. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
  5. 2018Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidatescited by 632
  6. 2017Non-Autoregressive Neural Machine Translationcited by 457
  7. 2013One Billion Word Benchmark for Measuring Progress in Statistical Language Modelingcited by 302
  8. 2017Lexically Constrained Decoding for Sequence Generation Using Grid Beam Searchcited by 234
  9. 2017Challenges in Data-to-Document Generationcited by 226
  10. 2016Incorporating Copying Mechanism in Sequence-to-Sequence Learningcited by 217
  11. 2018Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinementcited by 194
  12. 2018Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translationcited by 181
  13. 2020Reformulating Unsupervised Style Transfer as Paraphrase Generationcited by 181
  14. 2017Controllable Abstractive Summarizationcited by 110
  15. 2020CTRLsum: Towards Generic Controllable Text Summarizationcited by 98
  16. 2018CGMH: Constrained Sentence Generation by Metropolis-Hastings Samplingcited by 93
  17. 2020Logical Natural Language Generation from Open-Domain Tablescited by 90
  18. 2014On Using Very Large Target Vocabulary for Neural Machine Translationcited by 80
  19. 2018Learning Neural Templates for Text Generationcited by 61
  20. 2019Evaluating Style Transfer for Textcited by 59
  21. 2014Addressing the Rare Word Problem in Neural Machine Translationcited by 50
  22. 2016Pointing the Unknown Wordscited by 42
  23. 2016Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversationcited by 35
  24. 2019Text Generation with Exemplar-based Adaptive Decodingcited by 28
  25. 2021Parallel Refinements for Lexically Constrained Text Generation with BARTcited by 24
  26. 2020Posterior Control of Blackbox Generationcited by 16
  27. 2020Latent Template Induction with Gumbel-CRFscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2025Flexible-length Text Infilling for Discrete Diffusion Modelscited by 11
  2. 2024AmbigNLG: Addressing Task Ambiguity in Instruction for NLGcited by 10
  3. 2025WildIFEval: Instruction Following in the Wildcited by 10
  4. 2024Control Large Language Models via Divide and Conquercited by 5
  5. 2024Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrasescited by 3
  6. 2026In-Place Instruction Following in Diffusion Language Modelscited by 0