Few-Shot Document-Level Relation Extraction

• URL: http://arxiv.org/abs/2205.02048v1
• Date: Wed, 4 May 2022 13:16:19 GMT
• Title: Few-Shot Document-Level Relation Extraction
• Authors: Nicholas Popovic, Michael F\"arber
• Abstract summary: We present document-level relation extraction benchmark (FSDLRE) We argue that document-level corpora provide more realism, particularly regarding none-of-the-above (NOTA) distributions. We adapt the state-of-the-art sentence-level method MNAV to the document-level and develop it further for improved domain adaptation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. As opposed to existing benchmarks which are built on sentence-level relation extraction corpora, we argue that document-level corpora provide more realism, particularly regarding none-of-the-above (NOTA) distributions. Therefore, we propose a set of FSDLRE tasks and construct a benchmark based on two existing supervised learning data sets, DocRED and sciERC. We adapt the state-of-the-art sentence-level method MNAV to the document-level and develop it further for improved domain adaptation. We find FSDLRE to be a challenging setting with interesting new characteristics such as the ability to sample NOTA instances from the support set. The data, code, and trained models are available online (https://github.com/nicpopovic/FREDo).

Related papers

• READoc: A Unified Benchmark for Realistic Document Structured Extraction [44.44722729958791]
  We introduce a novel benchmark named READoc, which defines DSE as a realistic task. The READoc dataset is derived from 2,233 diverse and real-world documents from arXiv and GitHub. In addition, we develop a unified evaluation of state-of-the-art DSE approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:42:48Z)
• 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)
• The Power of Summary-Source Alignments [62.76959473193149]
  Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection. alignment of corresponding sentences between a reference summary and its source documents has been leveraged to generate training data. This paper proposes extending the summary-source alignment framework by applying it at the more fine-grained proposition span level.
  arXiv  Detail & Related papers  (2024-06-02T19:35:19Z)
• Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Models [29.94694305204144]
  We present a novel framework for document-level in-context few-shot relation extraction. We evaluate our framework using DocRED, the largest publicly available dataset for document-level relation extraction.
  arXiv  Detail & Related papers  (2023-10-17T09:10:27Z)
• 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)
• Long Document Summarization with Top-down and Bottom-up Inference [113.29319668246407]
  We propose a principled inference framework to improve summarization models on two aspects. Our framework assumes a hierarchical latent structure of a document where the top-level captures the long range dependency. We demonstrate the effectiveness of the proposed framework on a diverse set of summarization datasets.
  arXiv  Detail & Related papers  (2022-03-15T01:24:51Z)
• FLERT: Document-Level Features for Named Entity Recognition [5.27294900215066]
  Current state-of-the-art approaches for named entity recognition (NER) typically consider text at the sentence-level. The use of transformer-based models for NER offers natural options for capturing document-level features.
  arXiv  Detail & Related papers  (2020-11-13T16:13:59Z)
• Document-level Neural Machine Translation with Document Embeddings [82.4684444847092]
  This work focuses on exploiting detailed document-level context in terms of multiple forms of document embeddings. The proposed document-aware NMT is implemented to enhance the Transformer baseline by introducing both global and local document-level clues on the source end.
  arXiv  Detail & Related papers  (2020-09-16T19:43:29Z)
• Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline [94.0601799665342]
  Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task. We propose establishing summary-source alignment as an explicit task, while introducing two major novelties. We create a novel training dataset for proposition-level alignment, derived automatically from available summarization evaluation data. We present a supervised proposition alignment baseline model, showing improved alignment-quality over the unsupervised approach.
  arXiv  Detail & Related papers  (2020-09-01T17:27:12Z)
• Towards Making the Most of Context in Neural Machine Translation [112.9845226123306]
  We argue that previous research did not make a clear use of the global context. We propose a new document-level NMT framework that deliberately models the local context of each sentence.
  arXiv  Detail & Related papers  (2020-02-19T03:30:00Z)

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.