  The dialogue-based relation extraction (DialogRE) task aims to predict the
relations between argument pairs that appear in dialogue. Most previous studies
utilize fine-tuning pre-trained language models (PLMs) only with extensive
features to supplement the low information density of the dialogue by multiple
speakers. To effectively exploit inherent knowledge of PLMs without extra
layers and consider scattered semantic cues on the relation between the
arguments, we propose a Guiding model with RelAtional Semantics using Prompt
(GRASP). We adopt a prompt-based fine-tuning approach and capture relational
semantic clues of a given dialogue with 1) an argument-aware prompt marker
strategy and 2) the relational clue detection task. In the experiments, GRASP
achieves state-of-the-art performance in terms of both F1 and F1c scores on a
DialogRE dataset even though our method only leverages PLMs without adding any
extra layers.
