Zero-shot Entity Linking with Efficient Long Range Sequence Modeling

10/12/2020 ∙ by Zonghai Yao, et al. ∙ 0

This paper considers the problem of zero-shot entity linking, in which a link in the test time may not present in training. Following the prevailing BERT-based research efforts, we find a simple yet effective way is to expand the long-range sequence modeling. Unlike many previous methods, our method does not require expensive pre-training of BERT with long position embedding. Instead, we propose an efficient position embeddings initialization method called Embedding-repeat, which initializes larger position embeddings based on BERT-Base. On Wikia's zero-shot EL dataset, our method improves the SOTA from 76.06 74.57 sequence modeling without retraining the BERT model.



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