mixSeq: A Simple Data Augmentation Methodfor Neural Machine Translation
Xueqing Wu, Yingce Xia, Jinhua Zhu, Lijun Wu, Shufang Xie, Fan Yang, Tao Qin · 2021
Data augmentation, which refers to manipulating the inputs (e.g., adding random noise, masking specific parts) to enlarge the dataset, has been widely adopted in machine learning.Most data augmentation techniques operate on a single input, which limits the diversity of the training corpus.In this paper, we propose a simple yet effective data augmentation technique for neural machine translation, mixSeq, which operates on multiple inputs and their corresponding targets.Specifically, we randomly select two input sequences, concatenate them together as a longer input as well as their corresponding target sequences as an enlarged target, and train models on the augmented dataset.Experiments on nine machine translation tasks demonstrate that such a simple method boosts the baselines by a nontrivial margin.Our method can be further combined with single-input based data augmentation methods to obtain further improvements.