论文标题
NC-DRE：利用非实体线索信息进行文档级关系提取
NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction
论文作者
论文摘要
文档级关系提取（RE）需要对不同句子中的多个实体进行推理才能识别复杂的句子间关系，比句子级别更具挑战性。为了提取复杂的句子间关系，以前的研究通常采用图形神经网络（GNN）对异质文档图进行推断。尽管取得了巨大的成功，但这些基于图的方法通常仅考虑构建图和推理过程中提及的单词，倾向于忽略提及中没有的非实体线索词，而是为关系推理提供重要的线索信息。为了减轻这个问题，我们将基于图的文档级RE模型视为编码器数据框架，该框架通常使用预先培训的语言模型作为编码器，将GNN模型作为解码器，并提出了一种基于图形的NC模型NC-DRE，该模型NC-DRE介绍了解码器到解码器的注意机制，以驱动非实质性线索信息进行文档划分，以驱动文档级别的划分。
Document-level relation extraction (RE), which requires reasoning on multiple entities in different sentences to identify complex inter-sentence relations, is more challenging than sentence-level RE. To extract the complex inter-sentence relations, previous studies usually employ graph neural networks (GNN) to perform inference upon heterogeneous document-graphs. Despite their great successes, these graph-based methods, which normally only consider the words within the mentions in the process of building graphs and reasoning, tend to ignore the non-entity clue words that are not in the mentions but provide important clue information for relation reasoning. To alleviate this problem, we treat graph-based document-level RE models as an encoder-decoder framework, which typically uses a pre-trained language model as the encoder and a GNN model as the decoder, and propose a novel graph-based model NC-DRE that introduces decoder-to-encoder attention mechanism to leverage Non-entity Clue information for Document-level Relation Extraction.
