Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation

• URL: http://arxiv.org/abs/2203.07735v2
• Date: Wed, 16 Mar 2022 09:29:55 GMT
• Title: Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation
• Authors: Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park
• Abstract summary: Document Augmentation for dense Retrieval (DAR) framework augments the representations of documents with their Dense Augmentation and perturbations. We validate the performance of DAR on retrieval tasks with two benchmark datasets, showing that the proposed DAR significantly outperforms relevant baselines on the dense retrieval of both the labeled and unlabeled documents.
• Score: 49.940525611640346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dense retrieval models, which aim at retrieving the most relevant document for an input query on a dense representation space, have gained considerable attention for their remarkable success. Yet, dense models require a vast amount of labeled training data for notable performance, whereas it is often challenging to acquire query-document pairs annotated by humans. To tackle this problem, we propose a simple but effective Document Augmentation for dense Retrieval (DAR) framework, which augments the representations of documents with their interpolation and perturbation. We validate the performance of DAR on retrieval tasks with two benchmark datasets, showing that the proposed DAR significantly outperforms relevant baselines on the dense retrieval of both the labeled and unlabeled documents.

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