Document-level relation extraction with three channels

TL;DR: This paper proposes DRETC, a document-level relation extraction model with three channels, each extracting relation features from a different perspective, achieving better performance than previous models on the DocRED dataset through channel combination.

Abstract: Relation extraction aims to identify the relation between two entities. Early works on relation extraction are performed within a sentence. Numerous entity pairs with relations are distributed across sentences, so the task of document-level relation extraction is proposed. The current works perform well on document-level relation extraction through graph neural network, transformer, etc. However, these works extract relation features from one perspective, resulting in limited extraction performance of the model. To break through the limitation, this paper proposes the DRETC model consisting of three channels. Channel one first constructs the mention matrix and then extracts the relation features between entities. Channel two performs sequence inference from mention to entity before generating the relation matrix. Moreover, the bridge method is proposed to introduce information in channel one that is beneficial to relation extraction. Channel three directly generates entity representations through text sequences to make up for the absence of text information related to the entity. The three channels extract relation features from three different perspectives, and the combination leads to better extraction performance. Experimental results show that the proposed DRETC model outperforms previous strong baseline models with one channel on the public dataset DocRED. Further analysis demonstrates that the combination of multiple channels has a better relation extraction performance than a single channel.