Highly Parallel Autoregressive Entity Linking with Discriminative Correction (2021.emnlp-main)
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| Challenge: | Existing approaches to EL have been shown to be effective for both Entity Disambiguation and Entity Linking, but they suffer from high computational cost due to a complex (deep) decoder and the need for training on a large amount of data. |
| Approach: | They propose a method that parallelizes autoregressive linking across all potential mentions and relies on a shallow and efficient decoder. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on the English dataset AIDA-CoNLL and is >70 times faster and more accurate than the previous generative method. |
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| Challenge: | Existing methods for entity linking do not use a knowledge base or candidate sets. |
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Improving Entity Linking by Modeling Latent Relations between Mentions (P18-1)
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| Challenge: | Entity linking systems often exploit relations between textual mentions to decide if the linking decisions are compatible. |
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