  Dialogue relation extraction (DRE) aims to extract relations between two
arguments within a dialogue, which is more challenging than standard RE due to
the higher person pronoun frequency and lower information density in dialogues.
However, existing DRE methods still suffer from two serious issues: (1) hard to
capture long and sparse multi-turn information, and (2) struggle to extract
golden relations based on partial dialogues, which motivates us to discover
more effective methods that can alleviate the above issues. We notice that the
rise of large language models (LLMs) has sparked considerable interest in
evaluating their performance across diverse tasks. To this end, we initially
investigate the capabilities of different LLMs in DRE, considering both
proprietary models and open-source models. Interestingly, we discover that LLMs
significantly alleviate two issues in existing DRE methods. Generally, we have
following findings: (1) scaling up model size substantially boosts the overall
DRE performance and achieves exceptional results, tackling the difficulty of
capturing long and sparse multi-turn information; (2) LLMs encounter with much
smaller performance drop from entire dialogue setting to partial dialogue
setting compared to existing methods; (3) LLMs deliver competitive or superior
performances under both full-shot and few-shot settings compared to current
state-of-the-art; (4) LLMs show modest performances on inverse relations but
much stronger improvements on general relations, and they can handle dialogues
of various lengths especially for longer sequences.
