Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model (2021.acl-long)
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| Challenge: | Existing methods for fine-grained entity typing use weak labels that are automatically generated. |
| Approach: | They propose to obtain training data by using a BERT Masked Language Model (MLM) given a mention in a sentence, they construct an input for the MLM so it predicts context dependent hypernyms of the mention, which can be used as type labels. |
| Outcome: | The proposed model improves performance by using type labels generated from a BERT Masked Language Model given a mention in a sentence. |
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