Towards Better Entity Linking with Multi-View Enhanced Distillation

• URL: http://arxiv.org/abs/2305.17371v1
• Date: Sat, 27 May 2023 05:15:28 GMT
• Title: Towards Better Entity Linking with Multi-View Enhanced Distillation
• Authors: Yi Liu, Yuan Tian, Jianxun Lian, Xinlong Wang, Yanan Cao, Fang Fang, Wen Zhang, Haizhen Huang, Denvy Deng and Qi Zhang
• Abstract summary: This paper proposes a Multi-View Enhanced Distillation (MVD) framework for entity linking. MVD can effectively transfer knowledge of multiple fine-grained and mention-relevant parts within entities from cross-encoders to dual-encoders. Experiments show our method achieves state-of-the-art performance on several entity linking benchmarks.
• Score: 30.554387215553238
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Dense retrieval is widely used for entity linking to retrieve entities from large-scale knowledge bases. Mainstream techniques are based on a dual-encoder framework, which encodes mentions and entities independently and calculates their relevances via rough interaction metrics, resulting in difficulty in explicitly modeling multiple mention-relevant parts within entities to match divergent mentions. Aiming at learning entity representations that can match divergent mentions, this paper proposes a Multi-View Enhanced Distillation (MVD) framework, which can effectively transfer knowledge of multiple fine-grained and mention-relevant parts within entities from cross-encoders to dual-encoders. Each entity is split into multiple views to avoid irrelevant information being over-squashed into the mention-relevant view. We further design cross-alignment and self-alignment mechanisms for this framework to facilitate fine-grained knowledge distillation from the teacher model to the student model. Meanwhile, we reserve a global-view that embeds the entity as a whole to prevent dispersal of uniform information. Experiments show our method achieves state-of-the-art performance on several entity linking benchmarks.

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