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The dialogue-based relation extraction (DialogRE) task aims to predict therelations between argument pairs that appear in dialogue. Most previous studiesutilize fine-tuning pre-trained language models (PLMs) only with extensivefeatures to supplement the low information density of the dialogue by multiplespeakers. To effectively exploit inherent knowledge of PLMs without extralayers and consider scattered semantic cues on the relation between thearguments, we propose a Guiding model with RelAtional Semantics using Prompt(GRASP). We adopt a prompt-based fine-tuning approach and capture relationalsemantic clues of a given dialogue with 1) an argument-aware prompt markerstrategy and 2) the relational clue detection task. In the experiments, GRASPachieves state-of-the-art performance in terms of both F1 and F1c scores on aDialogRE dataset even though our method only leverages PLMs without adding anyextra layers.

GRASP: Guiding model with RelAtional Semantics using Prompt for Dialogue Relation Extraction