Document-Level Relation Extraction with Sentences Importance Estimation and Focusing (2022)

Abstract: Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences. Recent studies typically represent the entire document by sequence- or graph-based models to predict the relations of all entity pairs. However, we find that such a model is not robust and exhibits bizarre behaviors: it predicts correctly when an entire test document is fed as input, but errs when non-evidence sentences are removed. To this end, we propose a Sentence Importance Estimation and Focusing (SIEF) framework for DocRE, where we design a sentence importance score and a sentence focusing loss, encouraging DocRE models to focus on evidence sentences. Experimental results on two domains show that our SIEF not only improves overall performance, but also makes DocRE models more robust. Moreover, SIEF is a general framework, shown to be effective when combined with a variety of base DocRE models.

## related work relation extraction (re) can be categorized by its granularity, such as sentence-level @xcite @xcite and document-level @xcite . early work mainly focuses on sentence-level relation extraction. @xcite propose a rule-based approach, and @xcite manually design features for classifying relations. in the past several years, neural networks have become a prevailing approach for relation extraction @xcite . ## problem definition in this section, we present the formulation of document relation extraction @xcite . consider an unstructured document comprising n sentences, @xmath0, s 2 , • • • , s n }, where each sentence s n is a sequence words. in a docre dataset, the document d is typically annotated with entity mentions, each mention (e.g., u.s. and usa) labeled by its conceptual entity e and its entity type (e.g., location). a docre model f is usually formulated as multi-label classification @xcite . f j predicts whether the jth relation holds for the ith marked entity pair in a document, given by during inference, we obtain the relation(s) of a given entity pair by thresholding the predicted probabilities, following most previous work @xcite . ## methodology in this section, we will describe our approach in detail. the overview of our framework is shown in figure 2 . first, we describe the estimation of sentence importance in section 4.1. sentences with low importance scores are treated as non-evidence. then, sections 4.2 and 4.3 present our approach that encourages the model to produce the same output distribution, when the entire document is fed as input and when non-evidence sentences are removed. section 4.4 further presents the architectures of docre models.