Revisiting Sparse Retrieval for Few-shot Entity Linking
Yulin Chen Zhenran Xu Baotian Hu Min Zhang
Entity linking aims to link ambiguous mentions to their corresponding
entities in a knowledge base. One of the key challenges comes from insufficient
labeled data for specific domains. Although dense retrievers have achieved
excellent performance on several benchmarks, their performance decreases
significantly when only a limited amount of in-domain labeled data is
available. In such few-shot setting, we revisit the sparse retrieval method,
and propose an ELECTRA-based keyword extractor to denoise the mention context
and construct a better query expression. For training the extractor, we propose
a distant supervision method to automatically generate training data based on
overlapping tokens between mention contexts and entity descriptions.
Experimental results on the ZESHEL dataset demonstrate that the proposed method
outperforms state-of-the-art models by a significant margin across all test
domains, showing the effectiveness of keyword-enhanced sparse retrieval.