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2022 ACL ACL 2022

Word-level Perturbation Considering Word Length and Compositional Subwords

Abstract

AbstractWe present two simple modifications for word-level perturbation: Word Replacement considering Length (WR-L) and Compositional Word Replacement (CWR).In conventional word replacement, a word in an input is replaced with a word sampled from the entire vocabulary, regardless of the length and context of the target word.WR-L considers the length of a target word by sampling words from the Poisson distribution.CWR considers the compositional candidates by restricting the source of sampling to related words that appear in subword regularization. Experimental results showed that the combination of WR-L and CWR improved the performance of text classification and machine translation.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — word-level perturbation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Tatsuya Hiraoka , Sho Takase , Kei Uchiumi , Atsushi Keyaki , Naoaki Okazaki

Topics

Machine Learning > Application Areas > Data Augmentation Natural Language Processing > Applications > Machine Translation Natural Language Processing > Applications > Text Classification Deep Learning > Learning Types > Data Augmentation

Keywords

text classification machine translation data augmentation text augmentation subword regularization word replacement word-level perturbation
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