Papers with Generative approaches
Instructed Language Models with Retrievers Are Powerful Entity Linkers (2023.emnlp-main)
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| Challenge: | Generative approaches powered by large language models have demonstrated emergent abilities in tasks that require complex reasoning abilities. |
| Approach: | They propose a sequence-to-sequence training objective with instruction-tuning that enables casual language models to perform entity linking over knowledge bases. |
| Outcome: | The proposed framework outperforms existing approaches with +6.8 F1 points gain on average and huge advantage in training data efficiency and compute consumption. |
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. |