Multi-step Fusion of Relation Type Information and Multi-Task Decoding for Entity Relation Extraction in ancient Chinese

Abstract

Currently, there is limited research on entity relation extraction in the domain of ancient Chinese, primarily due to the scarcity of publicly available datasets for model training. To advance the study of entity relation extraction tasks in ancient Chinese, this paper reconstructs a CMAG-ERE2.0 dataset based on "Comprehensive Mirror to Aid in Government" Addressing issues such as missing relation predictions and inaccurate long-span entity boundary recognition prevalent in existing models applied to ancient Chinese texts, we propose a joint entity relation extraction model tailored for this context. In our model, relative position information of tokens enhances constraints on long-span entity boundaries, while multi-step fusion of relationship type information establishes connections between relational hints and entities, facilitating precise extraction of both long-span entities and latent relations. Additionally, we implement multi-task decoding for modeling purposes, which significantly improves the model’s ability to extract entities and relations within complex textual environments. Experimental results demonstrate that our proposed model effectively mitigates these challenges.