Data augmentation using back-translation for context-aware neural machine translation
By Amane Sugiyama, Naoki Yoshinaga. 2019
110 citations recorded by the source corpus.
A single sentence does not always convey information that is enough to translate it into other languages. Some target languages need to add or specialize words that are omitted or ambiguous in the source languages (e.g, zero pronouns in translating Japanese to English or epicene pronouns in translating English to French). To translate such ambiguous sentences, we need contexts beyond a single sentence, and have so far explored context-aware neural machine translation (NMT). However, a large amount of parallel corpora is not easily available to train accurate context-aware NMT models. In this study, we first obtain large-scale pseudo parallel corpora by back-translating monolingual data, and then investigate its impact on the translation accuracy of context-aware NMT models. We evaluated context-aware NMT models trained with small parallel corpora and the large-scale pseudo parallel corpora on English-Japanese and English-French datasets to demonstrate the large impact of the data augmentation for context-aware NMT models.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohit Bansal
- Graham Neubig
- Sebastian Gehrmann
- Noah A. Smith
- Yejin Choi
- Caiming Xiong
- Luke S. Zettlemoyer
- Eduard H. Hovy
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Dacheng Tao
- Andrew Zisserman
- Jitendra Malik
- Trevor Darrell
- Xiaogang Wang
- Ming-Hsuan Yang
- Eduard H. Hovy
In Natural Language Processing
- Sebastian Gehrmann
- Mohit Bansal
- Mirella Lapata
- Eduard H. Hovy
- Caiming Xiong
- Graham Neubig
- Yejin Choi
- Noah A. Smith
Citations in the corpus
- A Survey of Data Augmentation Approaches for NLP (2021, background): “Sugiyama and Yoshinaga (2019) Investigate effectiveness of DA by backtranslation for context-aware NMT. Li and Specia (2019) Present DA methods to improve NMT robustness to noise while keeping models small, and explore the use of noise from external data (speech transcripts).”
- NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation (2021, background): “Backtranslation has been quite popular now and has been a quick way to augment examples (Li and Specia, 2019; Sugiyama and Yoshinaga, 2019).”
- ReCode: Robustness Evaluation of Code Generation Models (2022, methodology): “It is a common method for data augmentation in generating sentence variances with the same semantics (Li and Specia, 2019; Sugiyama and Yoshinaga, 2019).”