mlsanityData to 7 Oct 2026

PARAGEN : A Parallel Generation Toolkit

arXiv 2210.03405 ↗First posted 7 Oct 2022cs.CLcited by 2

Not on the map. Why?

Abstract (arXiv)

PARAGEN is a PyTorch-based NLP toolkit for further development on parallel generation. PARAGEN provides thirteen types of customizable plugins, helping users to experiment quickly with novel ideas across model architectures, optimization, and learning strategies. We implement various features, such as unlimited data loading and automatic model selection, to enhance its industrial usage. ParaGen is now deployed to support various research and industry applications at ByteDance. PARAGEN is available at https://github.com/bytedance/ParaGen.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 29 of its 36 references that are in the map. First posted on arXiv 7 Oct 2022.

Builds on

29 of 36 references.

References · 29

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  7. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  8. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  9. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  10. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  11. 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,486
  12. 2022MaskGIT: Masked Generative Image Transformercited by 1,082
17 more
  1. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
  2. 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 710
  3. 2017Non-Autoregressive Neural Machine Translationcited by 457
  4. 2018Horovod: fast and easy distributed deep learning in TensorFlowcited by 275
  5. 2019FreeLB: Enhanced Adversarial Training for Natural Language Understandingcited by 261
  6. 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
  7. 2019Levenshtein Transformercited by 197
  8. 2020Glancing Transformer for Non-Autoregressive Neural Machine Translationcited by 103
  9. 2020Fully Non-autoregressive Neural Machine Translation: Tricks of the Tradecited by 99
  10. 2020Non-Autoregressive Machine Translation with Latent Alignmentscited by 96
  11. 2018Tensor2Tensor for Neural Machine Translationcited by 85
  12. 2020Imputer: Sequence Modelling via Imputation and Dynamic Programmingcited by 65
  13. 2021Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervisioncited by 40
  14. 2019Imitation Learning for Non-Autoregressive Neural Machine Translationcited by 36
  15. 2022CoNT: Contrastive Neural Text Generationcited by 21
  16. 2021LightSeq2: Accelerated Training for Transformer-based Models on GPUscited by 19
  17. 2021The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21cited by 14

Built on by

Cited by year

Citing papers per year: 2022: 2.

2 papers.