Papers with SeqMix
Sequence-Level Mixed Sample Data Augmentation (2020.emnlp-main)
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| Challenge: | Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. |
| Approach: | They propose a data augmentation approach to encourage compositional behavior in neural networks . they propose to softly combine input/output sequences from the training set . |
| Outcome: | The proposed approach yields 1.0 BLEU improvement on translation datasets over baselines. |
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup (2020.emnlp-main)
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| Challenge: | Existing active sequence labeling methods use the queried samples alone in each iteration, which is inefficient for leveraging human annotations. |
| Approach: | They propose a data augmentation method to augment queried samples by generating extra labeled sequences in each iteration. |
| Outcome: | The proposed method improves the standard active sequence labeling method by 2.27%–3.75% in terms of F1 scores. |