REXEL : An End-to-end Model for Document-Level Relation Extraction and Entity Linking (2024)

Abstract: Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by \textit{closed information extraction} (cIE). However, existing approaches for cIE suffer from two limitations: \textit{(i)} they are often pipelines which makes them prone to error propagation, and/or \textit{(ii)} they are restricted to sentence level which prevents them from capturing long-range dependencies and results in expensive inference time. We address these limitations by proposing REXEL, a highly efficient and accurate model for the joint task of document level cIE (DocIE). REXEL performs mention detection, entity typing, entity disambiguation, coreference resolution and document-level relation classification in a single forward pass to yield facts fully linked to a reference knowledge graph. It is on average 11 times faster than competitive existing approaches in a similar setting and performs competitively both when optimised for any of the individual sub-task and a variety of combinations of different joint tasks, surpassing the baselines by an average of more than 6 F1 points. The combination of speed and accuracy makes REXEL an accurate cost-efficient system for extracting structured information at web-scale. We also release an extension of the DocRED dataset to enable benchmarking of future work on DocIE, which will be available at https://github.com/amazon-science/e2e-docie.

## introduction extracting structured information from unstructured text is a critical step for many downstream nlp tasks like knowledge graph construction @xcite , question answering @xcite , knowledge discovery @xcite , and text summarization * work completed whilst at amazon alexa ai @xcite . in cie, this is defined as extracting an exhaustive set of (subject, relation, object) triples, or facts, from unstructured text that are fully linked, i.e., consistent with a predefined set of entities and relations from a knowledge graph (kg) schema. cie can be further decomposed into the subtasks: mention detection (md), entity typing (et), entity disambiguation (ed), and relation classification (rc). traditionally, cie is done by combining these subtasks sequentially @xcite , which involves the use of separate and often different models for each task to yield facts that can be ingested into a kg. however, such pipeline architectures are prone to error accumulation from each component leading to significant deterioration of the overall performance @xcite @xcite . additionally, pipeline architectures assume a one-way dependency between the subtasks, disregarding the dependencies among components that could effectively boost performance. for instance, while ed typically informs rc, recent works have demonstrated that rc information can also be effectively utilised for the ed task @xcite , and help preventing issues such as popular entities overshadowing less common entities @xcite . consequently, various joint/endto-end (e2e) systems have been proposed to address this issue by jointly performing ner and rc @xcite . this joint task is often referred to as relation extraction @xcite . however, these approaches do not address ed and thus do not yield facts fully linked to a kg. another drawback of existing approaches for cie is that they mostly operate at sentence level, i.e., perform rc between two entities from a single sentence at a time @xcite @xcite . thus, they capture limited sentence-level context and miss the facts that are expressed between entities across sentences. this severely limits the amount of information that can be extracted from the web. according to @xcite , 40.7% of the facts in a document can only be determined at the document level. also, sentencelevel approaches require a forward pass for each sentence, often leading to higher inference times, which makes them inefficient for web-scale applications. in contrast, document-level rc is computationally more efficient as it extracts triples over an entire document in a single forward pass. to address these issues, several models have been proposed for document-level rc @xcite @xcite but they do not perform the remaining subtasks needed for docie. to address the above problems we introduce rexel, a computationally efficient e2e model for docie. rexel takes unstructured text and extracts facts which are fully linked to a reference kg in a single forward pass per document. it has a modular architecture in which the various subtasks for docie inform each other by leveraging intermediate embedding representations. thus, the proposed framework facilitates deployment not only for docie but also for various combinations of its 5 subtasks (e.g., use md and et only for ner). the combination of modularity, fast inference speed and high accuracy makes rexel suitable for performing docie or its sub-tasks at industry scale. to summarize, our contributions are as follows: 1. we introduce rexel, a unified e2e model for docie, i.e., extracting facts at document level fully linked to a reference kg in a single forward pass per document. 2. we demonstrate that though rexel is optimised for the e2e task of docie, it maintains a competitive edge with related work in e2e re setting and all its individual subtasks. specifically, rexel improves upon the baselines for the e2e re task by an average @xmath0. when comparing the performance of individual subtasks, we observe that rexel outperforms the baselines by an average of 6 f1 points. 3. we also demonstrate that when compared to other e2e re models, in the same setting rexel is on average 11 times faster. 4. finally, we release an extension of the do-cred (yao et al., 2019) dataset released by (eberts and ulges, 2021) augmented with silver standard labels for entity linking to facilitate benchmarking of future work on docie. we name this extension docred-ie. ## rexel we introduce rexel (relation extraction and entity linking), a novel end-to-end model for do-cie. rexel extracts triples fully linked to a kg by jointly performing md, et, document level rc, coref and ed in a single forward pass. it combines the 5 subtasks in a unified architecture via intermediate embedding representations. this facilitates each task to inherently benefit from each other, significantly boosting task accuracy, extracting facts expressed across sentences, and maintaining computational efficiency for web-scale deployment. figure 1 illustrates the architecture and each module is detailed in following sections. ## results we summarize all results from single runs in