RETSim: Resilient and Efficient Text Similarity

• URL: http://arxiv.org/abs/2311.17264v1
• Date: Tue, 28 Nov 2023 22:54:33 GMT
• Title: RETSim: Resilient and Efficient Text Similarity
• Authors: Marina Zhang, Owen Vallis, Aysegul Bumin, Tanay Vakharia, Elie Bursztein
• Abstract summary: RETSim is a lightweight, multilingual deep learning model trained to produce robust metric embeddings for text retrieval, clustering, and dataset deduplication tasks. We demonstrate that RETSim is significantly more robust and accurate than MinHash and neural text embeddings. We also introduce the W4NT3D benchmark for evaluating multilingual, near-duplicate text retrieval capabilities under adversarial settings.
• Score: 1.6228944467258688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces RETSim (Resilient and Efficient Text Similarity), a lightweight, multilingual deep learning model trained to produce robust metric embeddings for near-duplicate text retrieval, clustering, and dataset deduplication tasks. We demonstrate that RETSim is significantly more robust and accurate than MinHash and neural text embeddings, achieving new state-of-the-art performance on dataset deduplication, adversarial text retrieval benchmarks, and spam clustering tasks. We also introduce the W4NT3D benchmark (Wiki-40B 4dversarial Near-T3xt Dataset) for evaluating multilingual, near-duplicate text retrieval capabilities under adversarial settings. RETSim and the W4NT3D benchmark are open-sourced under the MIT License at https://github.com/google/unisim.

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