Papers with relational clue detection task

1 papers
GRASP: Guiding Model with RelAtional Semantics Using Prompt for Dialogue Relation Extraction (2022.coling-1)

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Challenge: Existing studies utilize pre-trained language models with extensive features to supplement the low information density of the dialogue by multiple speakers.
Approach: They propose a dialogue-based relation extraction task that leverages pre-trained language models to capture relational semantic clues of a given dialogue using an argument-aware prompt marker strategy and a relational clue detection task.
Outcome: The proposed model achieves state-of-the-art on a DialogRE dataset even though it only leverages pre-trained language models without adding any extra layers.

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