mlsanityData to 14 Oct 2026

Back Translation Survey for Improving Text Augmentation

arXiv 2102.09708 ↗First posted 19 Feb 2021cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Natural Language Processing (NLP) relies heavily on training data. Transformers, as they have gotten bigger, have required massive amounts of training data. To satisfy this requirement, text augmentation should be looked at as a way to expand your current dataset and to generalize your models. One text augmentation we will look at is translation augmentation. We take an English sentence and translate it to another language before translating it back to English. In this paper, we look at the effect of 108 different language back translations on various metrics and text embeddings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 19 Feb 2021.

Builds on

12 of 31 references.

References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  5. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  6. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  7. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
  8. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  9. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  10. 2014Distributed Representations of Sentences and Documentscited by 678
  11. 2016An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generationcited by 48
  12. 2020How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?cited by 41

Built on by

Not cited yet.