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RETVec: Resilient and Efficient Text Vectorizer
April 24, 2024, 4:47 a.m. | Elie Bursztein, Marina Zhang, Owen Vallis, Xinyu Jia, Alexey Kurakin
cs.CL updates on arXiv.org arxiv.org
Abstract: This paper describes RETVec, an efficient, resilient, and multilingual text vectorizer designed for neural-based text processing. RETVec combines a novel character encoding with an optional small embedding model to embed words into a 256-dimensional vector space. The RETVec embedding model is pre-trained using pair-wise metric learning to be robust against typos and character-level adversarial attacks. In this paper, we evaluate and compare RETVec to state-of-the-art vectorizers and word embeddings on popular model architectures and datasets. …
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