Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory
By Hannah Cyberey, Yangfeng Ji, David E. Evans. 2020
15 citations recorded by the source corpus.
Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size. We propose methods for automatically improving datasets by viewing them as graphs with expected semantic properties. We construct a paraphrase graph from the provided sentence pair labels, and create an augmented dataset by directly inferring labels from the original sentence pairs using a transitivity property. We use structural balance theory to identify likely mislabelings in the graph, and flip their labels. We evaluate our methods on paraphrase models trained using these datasets starting from a pretrained BERT model, and find that the automatically-enhanced training sets result in more accurate models.
Subfields: Artificial Intelligence, 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
- Varun Prashant Gangal
- Soroush Vosoughi
- Eduard H. Hovy
- Teruko Mitamura
- Jason Wei
- Steven Y. Feng
- Sarath Chandar
- Mohit Bansal
In Natural Language Processing
- Varun Prashant Gangal
- Eduard H. Hovy
- Teruko Mitamura
- Soroush Vosoughi
- Jason Wei
- Steven Y. Feng
- Sarath Chandar
- Mohit Bansal
Citations in the corpus
- A Survey of Data Augmentation Approaches for NLP (2021, methodology): “Chen et al. (2020d) show that DA leads to variance reduction by averaging over orbits of the group that keep the data distribution approximately invariant.”