GraDA: Graph Generative Data Augmentation for Commonsense Reasoning

By Adyasha Maharana, Mohit Bansal. 2022

Subfields: Natural Language Processing, Graph Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Luke S. Zettlemoyer
2. Daniel S. Weld
3. Michael Lewis
4. Mohit Bansal
5. Percy Liang
6. Yejin Choi
7. Mandar Joshi
8. Shafiq R. Joty

In Graph Learning

1. Luke S. Zettlemoyer

Citations in the corpus

• Few-shot Mining of Naturally Occurring Inputs and Outputs (2022, methodology): “Data augmentation Data augmentation is a popular technique with a large body of work (Wang and Yang, 2015; Jia and Liang, 2016) et al., 2019), common-sense reasoning (Yang et al., 2020), and machine translation (Sennrich et al., 2016).”
• Is GPT-3 a Good Data Annotator? (2022, methodology): “, 2021), which involves generating new data by modifying existing data points using transformations based on prior knowledge about the problem’s structure (Yang et al., 2020).”