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arXiv · 2010.06065

Zero-shot Entity Linking with Efficient Long Range Sequence Modeling

Zonghai Yao·Liangliang Cao·Huapu Pan
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Abstract

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% to 79.08%, and for its long data, the corresponding improvement is from 74.57% to 82.14%. Our experiments suggest the effectiveness of long-range sequence modeling without retraining the BERT model.

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Zonghai Yao, Liangliang Cao, Huapu Pan. 2020-10-12. Zero-shot Entity Linking with Efficient Long Range Sequence Modeling. https://doi.org/10.18653/v1%2F2020.findings-emnlp.228

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