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.

对话关系提取（DRE）在提取对话中两个参数之间的关系方面比标准的关系提取更具挑战性，该研究发现大语言模型（LLMs）能够显著缓解现有 DRE 方法中的两个问题，通过提高模型规模可以显著提升 DRE 性能，对长且稀疏的多轮信息进行有效捕捉，并在全局对话和部分对话设置下均表现出竞争力强、制胜优势的性能。