@inproceedings{sawhney-etal-2021-dmix,
title = "{DM}ix: Distance Constrained Interpolative Mixup",
author = "Sawhney, Ramit and
Thakkar, Megh and
Pandit, Shrey and
Mukherjee, Debdoot and
Flek, Lucie",
editor = "Ataman, Duygu and
Birch, Alexandra and
Conneau, Alexis and
Firat, Orhan and
Ruder, Sebastian and
Sahin, Gozde Gul",
booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mrl-1.21",
doi = "10.18653/v1/2021.mrl-1.21",
pages = "242--244",
abstract = "Interpolation-based regularisation methods have proven to be effective for various tasks and modalities. Mixup is a data augmentation method that generates virtual training samples from convex combinations of individual inputs and labels. We extend Mixup and propose DMix, distance-constrained interpolative Mixup for sentence classification leveraging the hyperbolic space. DMix achieves state-of-the-art results on sentence classification over existing data augmentation methods across datasets in four languages.",
}
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<abstract>Interpolation-based regularisation methods have proven to be effective for various tasks and modalities. Mixup is a data augmentation method that generates virtual training samples from convex combinations of individual inputs and labels. We extend Mixup and propose DMix, distance-constrained interpolative Mixup for sentence classification leveraging the hyperbolic space. DMix achieves state-of-the-art results on sentence classification over existing data augmentation methods across datasets in four languages.</abstract>
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%0 Conference Proceedings
%T DMix: Distance Constrained Interpolative Mixup
%A Sawhney, Ramit
%A Thakkar, Megh
%A Pandit, Shrey
%A Mukherjee, Debdoot
%A Flek, Lucie
%Y Ataman, Duygu
%Y Birch, Alexandra
%Y Conneau, Alexis
%Y Firat, Orhan
%Y Ruder, Sebastian
%Y Sahin, Gozde Gul
%S Proceedings of the 1st Workshop on Multilingual Representation Learning
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F sawhney-etal-2021-dmix
%X Interpolation-based regularisation methods have proven to be effective for various tasks and modalities. Mixup is a data augmentation method that generates virtual training samples from convex combinations of individual inputs and labels. We extend Mixup and propose DMix, distance-constrained interpolative Mixup for sentence classification leveraging the hyperbolic space. DMix achieves state-of-the-art results on sentence classification over existing data augmentation methods across datasets in four languages.
%R 10.18653/v1/2021.mrl-1.21
%U https://aclanthology.org/2021.mrl-1.21
%U https://doi.org/10.18653/v1/2021.mrl-1.21
%P 242-244
Markdown (Informal)
[DMix: Distance Constrained Interpolative Mixup](https://aclanthology.org/2021.mrl-1.21) (Sawhney et al., MRL 2021)
ACL
- Ramit Sawhney, Megh Thakkar, Shrey Pandit, Debdoot Mukherjee, and Lucie Flek. 2021. DMix: Distance Constrained Interpolative Mixup. In Proceedings of the 1st Workshop on Multilingual Representation Learning, pages 242–244, Punta Cana, Dominican Republic. Association for Computational Linguistics.