Learn Before
Downstream NLP Tasks applying DA Techniques
- Summarization: Common Crawl, WikiRef, QMDSCNN, QMDSIR
- QA: XLDA, QANet
- Sequence Tagging Tasks: DAGA, dependency tree morphing, SEQMIX
- Parsing Tasks: Data Recombination, GRAPPA, compositionality
- Grammatical Error Correction: Use annotated data or confusion sets
- Neural Machine Translation: BackTranslation, SwitchOut, Soft Contextual, Data Diversification
- Data-to-Text NLG: perturbing language game scores or inputs, or adding noise.
- Open-Ended & Conditional Generation: GEAUNG
- Dialogue: LIGHTWEIGHT AUGMENTATION, diversity rank, MADA, spoken language understanding
- Multimodal Tasks: Augment parallel data seperately and reinforce the alignment.
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Data Science
Learn After
BackTranslation
WikiRef: Wikilinks as a route to recommending appropriate references for scientific Wikipedia pages
Coarse-to-Fine Query Focused Multi-Document Summarization
XLDA
XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
QANet
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks
DAGA
Dependency Tree Morphing
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation
SwitchOut
Soft Contextual Data Augmentation for Neural Machine Translation
Soft Contextual
Data Diversification: A Simple Strategy For Neural Machine Translation
Data Diversification