COLING 2020main32 citations
Augmenting NLP models using Latent Feature Interpolations
Amit Jindal, Arijit Ghosh Chowdhury, Aniket Didolkar, Di Jin, Ramit Sawhney, Rajiv Ratn Shah
Abstract
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.
BibTeX
@inproceedings{jindal-etal-2020-augmenting,
title = "Augmenting {NLP} models using Latent Feature Interpolations",
author = "Jindal, Amit and
Ghosh Chowdhury, Arijit and
Didolkar, Aniket and
Jin, Di and
Sawhney, Ramit and
Shah, Rajiv Ratn",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.611/",
doi = "10.18653/v1/2020.coling-main.611",
pages = "6931--6936"
}