Fine-Grained Distillation for Long Document Retrieval

• URL: http://arxiv.org/abs/2212.10423v1
• Date: Tue, 20 Dec 2022 17:00:36 GMT
• Title: Fine-Grained Distillation for Long Document Retrieval
• Authors: Yucheng Zhou, Tao Shen, Xiubo Geng, Chongyang Tao, Guodong Long, Can Xu, Daxin Jiang
• Abstract summary: Long document retrieval aims to fetch query-relevant documents from a large-scale collection. Knowledge distillation has become de facto to improve a retriever by mimicking a heterogeneous yet powerful cross-encoder. We propose a new learning framework, fine-grained distillation (FGD), for long-document retrievers.
• Score: 86.39802110609062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a heterogeneous yet powerful cross-encoder. However, in contrast to passages or sentences, retrieval on long documents suffers from the scope hypothesis that a long document may cover multiple topics. This maximizes their structure heterogeneity and poses a granular-mismatch issue, leading to an inferior distillation efficacy. In this work, we propose a new learning framework, fine-grained distillation (FGD), for long-document retrievers. While preserving the conventional dense retrieval paradigm, it first produces global-consistent representations crossing different fine granularity and then applies multi-granular aligned distillation merely during training. In experiments, we evaluate our framework on two long-document retrieval benchmarks, which show state-of-the-art performance.

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