Eider: Evidence-enhanced Document-level Relation Extraction

• URL: http://arxiv.org/abs/2106.08657v1
• Date: Wed, 16 Jun 2021 09:43:16 GMT
• Title: Eider: Evidence-enhanced Document-level Relation Extraction
• Authors: Yiqing Xie, Jiaming Shen, Sha Li, Yuning Mao, Jiawei Han
• Abstract summary: Document-level relation extraction (DocRE) aims at extracting semantic relations among entity pairs in a document. We propose a three-stage evidence-enhanced DocRE framework consisting of joint relation and evidence extraction, evidence-centered relation extraction (RE), and fusion of extraction results.
• Score: 56.71004595444816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document-level relation extraction (DocRE) aims at extracting the semantic relations among entity pairs in a document. In DocRE, a subset of the sentences in a document, called the evidence sentences, might be sufficient for predicting the relation between a specific entity pair. To make better use of the evidence sentences, in this paper, we propose a three-stage evidence-enhanced DocRE framework consisting of joint relation and evidence extraction, evidence-centered relation extraction (RE), and fusion of extraction results. We first jointly train an RE model with a simple and memory-efficient evidence extraction model. Then, we construct pseudo documents based on the extracted evidence sentences and run the RE model again. Finally, we fuse the extraction results of the first two stages using a blending layer and make a final prediction. Extensive experiments show that our proposed framework achieves state-of-the-art performance on the DocRED dataset, outperforming the second-best method by 0.76/0.82 Ign F1/F1. In particular, our method significantly improves the performance on inter-sentence relations by 1.23 Inter F1.

Related papers

• GEGA: Graph Convolutional Networks and Evidence Retrieval Guided Attention for Enhanced Document-level Relation Extraction [15.246183329778656]
  Document-level relation extraction (DocRE) aims to extract relations between entities from unstructured document text. To overcome these challenges, we propose GEGA, a novel model for DocRE. We evaluate the GEGA model on three widely used benchmark datasets: DocRED, Re-DocRED, and Revisit-DocRED.
  arXiv  Detail & Related papers  (2024-07-31T07:15:33Z)
• DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction [13.076163309118702]
  Evidence retrieval in document-level relation extraction (DocRE) faces two major issues: high memory consumption and limited availability of annotations. We propose DREEAM, a memory-efficient approach that adopts evidence information as the supervisory signal. Experimental results reveal that our approach exhibits state-of-the-art performance on the DocRED benchmark for both DocRE and ER.
  arXiv  Detail & Related papers  (2023-02-17T03:54:31Z)
• Document-Level Relation Extraction with Sentences Importance Estimation and Focusing [52.069206266557266]
  Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences. We propose a Sentence Estimation and Focusing (SIEF) framework for DocRE, where we design a sentence importance score and a sentence focusing loss. Experimental results on two domains show that our SIEF not only improves overall performance, but also makes DocRE models more robust.
  arXiv  Detail & Related papers  (2022-04-27T03:20:07Z)
• GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
  We propose GERE, the first system that retrieves evidences in a generative fashion. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-04-12T03:49:35Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• D-REX: Dialogue Relation Extraction with Explanations [65.3862263565638]
  This work focuses on extracting explanations that indicate that a relation exists while using only partially labeled data. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that explains and ranks relations. We find that about 90% of the time, human annotators prefer D-REX's explanations over a strong BERT-based joint relation extraction and explanation model.
  arXiv  Detail & Related papers  (2021-09-10T22:30:48Z)
• A Multi-Level Attention Model for Evidence-Based Fact Checking [58.95413968110558]
  We present a simple model that can be trained on sequence structures. Results on a large-scale dataset for Fact Extraction and VERification show that our model outperforms the graph-based approaches.
  arXiv  Detail & Related papers  (2021-06-02T05:40:12Z)
• Entity and Evidence Guided Relation Extraction for DocRED [33.69481141963074]
  We pro-pose a joint training frameworkE2GRE(Entity and Evidence Guided Relation Extraction)for this task. We introduce entity-guided sequences as inputs to a pre-trained language model (e.g. BERT, RoBERTa) These entity-guided sequences help a pre-trained language model (LM) to focus on areas of the document related to the entity. We evaluate our E2GRE approach on DocRED, a recently released large-scale dataset for relation extraction.
  arXiv  Detail & Related papers  (2020-08-27T17:41:23Z)
• Reasoning with Latent Structure Refinement for Document-Level Relation Extraction [20.308845516900426]
  We propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. Specifically, our model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED)
  arXiv  Detail & Related papers  (2020-05-13T13:36:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.