Augmenting NLP models using Latent Feature Interpolations
By Amit Jindal, Arijit Ghosh Chowdhury, Aniket Rajiv Didolkar, Di Jin, Ramit Sawhney, Rajiv Ratn Shah. 2020
30 citations recorded by the source corpus.
Models with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution. We introduce Emix, a data augmentation method that uses interpolations of word embeddings and hidden layer representations to construct virtual examples. We show that Emix shows significant improvements over previously used interpolation based regularizers and data augmentation techniques. We also demonstrate how our proposed method is more robust to sparsification. We highlight the merits of our proposed methodology by performing thorough quantitative and qualitative assessments.
Subfields: Natural Language Processing
How researchers see this paper
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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, background): “Later variants propose speech-tailored mixing schemes (Jindal et al., 2020b) and interpolation with adversarial examples (Cheng et al., 2020), among others.”