Relation extraction is a critical task in the field of natural language
processing with numerous real-world applications. Existing research primarily
focuses on monolingual relation extraction or cross-lingual enhancement for
relation extraction. Yet, there remains a significant gap in understanding
relation extraction in the mix-lingual (or code-switching) scenario, where
individuals intermix contents from different languages within sentences,
generating mix-lingual content. Due to the lack of a dedicated dataset, the
effectiveness of existing relation extraction models in such a scenario is
largely unexplored. To address this issue, we introduce a novel task of
considering relation extraction in the mix-lingual scenario called MixRE and
constructing the human-annotated dataset MixRED to support this task. In
addition to constructing the MixRED dataset, we evaluate both state-of-the-art
supervised models and large language models (LLMs) on MixRED, revealing their
respective advantages and limitations in the mix-lingual scenario. Furthermore,
we delve into factors influencing model performance within the MixRE task and
uncover promising directions for enhancing the performance of both supervised
models and LLMs in this novel task.

研究论文介绍了在混合语言场景中进行关系抽取的新任务 MixRE，并构建了混合语言数据集 MixRED，评估了现有的有监督模型和大型语言模型对于这一新任务的表现，并揭示了影响模型性能的因素以及提高模型性能的有望方向。