Improving short text classification through global augmentation methods
Abstract
We study the effect of different approaches to text augmentation. To do this we use 3 datasets that include social media and formal text in the form of news articles. Our goal is to provide insights for practitioners and researchers on making choices for augmentation for classification use cases. We observe that Word2vec-based augmentation is a viable option when one does not have access to a formal synonym model (like WordNet-based augmentation). The use of \emph{mixup} further improves performance of all text based augmentations and reduces the effects of overfitting on a tested deep learning model. Round-trip translation with a translation service proves to be harder to use due to cost and as such is less accessible for both normal and low resource use-cases.
- Publication:
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arXiv e-prints
- Pub Date:
- July 2019
- DOI:
- 10.48550/arXiv.1907.03752
- arXiv:
- arXiv:1907.03752
- Bibcode:
- 2019arXiv190703752M
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Information Retrieval;
- Computer Science - Machine Learning
- E-Print:
- Final version published in CD-MAKE 2020: Machine Learning and Knowledge Extraction pp 385-399