Papers with SAIS

1 papers
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction (2022.naacl-main)

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Challenge: Existing methods for relation extraction only implicitly learn to model relevant contexts and entity types while being trained for RE.
Approach: They propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for RE.
Outcome: The proposed method outperforms the runner-up method on three benchmarks by 5.04% . textual contexts and entity types are the major information sources that lead to the success of previous approaches.

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