PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for
End-to-end Document Pair Extraction
- URL: http://arxiv.org/abs/2401.03472v1
- Date: Sun, 7 Jan 2024 12:48:07 GMT
- Title: PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for
End-to-end Document Pair Extraction
- Authors: Zening Lin, Jiapeng Wang, Teng Li, Wenhui Liao, Dayi Huang, Longfei
Xiong, Lianwen Jin
- Abstract summary: Document pair extraction aims to identify key and value entities as well as their relationships from visually-rich documents.
Most existing methods divide it into two separate tasks: semantic entity recognition (SER) and relation extraction (RE)
This paper introduces PEneo, which performs document pair extraction in a unified pipeline, incorporating three concurrent sub-tasks: line extraction, line grouping, and entity linking.
- Score: 29.620120164447737
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Document pair extraction aims to identify key and value entities as well as
their relationships from visually-rich documents. Most existing methods divide
it into two separate tasks: semantic entity recognition (SER) and relation
extraction (RE). However, simply concatenating SER and RE serially can lead to
severe error propagation, and it fails to handle cases like multi-line entities
in real scenarios. To address these issues, this paper introduces a novel
framework, PEneo (Pair Extraction new decoder option), which performs document
pair extraction in a unified pipeline, incorporating three concurrent
sub-tasks: line extraction, line grouping, and entity linking. This approach
alleviates the error accumulation problem and can handle the case of multi-line
entities. Furthermore, to better evaluate the model's performance and to
facilitate future research on pair extraction, we introduce RFUND, a
re-annotated version of the commonly used FUNSD and XFUND datasets, to make
them more accurate and cover realistic situations. Experiments on various
benchmarks demonstrate PEneo's superiority over previous pipelines, boosting
the performance by a large margin (e.g., 19.89%-22.91% F1 score on RFUND-EN)
when combined with various backbones like LiLT and LayoutLMv3, showing its
effectiveness and generality. Codes and the new annotations will be open to the
public.
Related papers
- AutoRE: Document-Level Relation Extraction with Large Language Models [27.426703757501507]
We introduce AutoRE, an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts)
Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios.
Our experiments on the RE-DocRED dataset showcase AutoRE's best performance, achieving state-of-the-art results.
arXiv Detail & Related papers (2024-03-21T23:48:21Z) - List-aware Reranking-Truncation Joint Model for Search and
Retrieval-augmented Generation [80.12531449946655]
We propose a Reranking-Truncation joint model (GenRT) that can perform the two tasks concurrently.
GenRT integrates reranking and truncation via generative paradigm based on encoder-decoder architecture.
Our method achieves SOTA performance on both reranking and truncation tasks for web search and retrieval-augmented LLMs.
arXiv Detail & Related papers (2024-02-05T06:52:53Z) - M$^3$Net: Multi-view Encoding, Matching, and Fusion for Few-shot
Fine-grained Action Recognition [80.21796574234287]
M$3$Net is a matching-based framework for few-shot fine-grained (FS-FG) action recognition.
It incorporates textitmulti-view encoding, textitmulti-view matching, and textitmulti-view fusion to facilitate embedding encoding, similarity matching, and decision making.
Explainable visualizations and experimental results demonstrate the superiority of M$3$Net in capturing fine-grained action details.
arXiv Detail & Related papers (2023-08-06T09:15:14Z) - PPN: Parallel Pointer-based Network for Key Information Extraction with
Complex Layouts [29.73609439825548]
Key Information Extraction is a challenging task that aims to extract structured value semantic entities from documents.
Existing methods follow a two-stage pipeline strategy, which may lead to the error propagation problem.
We introduce Parallel Pointer-based Network (PPN), an end-to-end model that can be applied in zero-shot and few-shot scenarios.
arXiv Detail & Related papers (2023-07-20T03:29:09Z) - Mutually Guided Few-shot Learning for Relational Triple Extraction [10.539566491939844]
Mutually Guided Few-shot learning framework for Triple Extraction (MG-FTE)
Our method consists of an entity-guided relation-decoder to classify relations and a proto-decoder to extract entities.
Our method outperforms many state-of-the-art methods by 12.6 F1 score on FewRel 1.0 (single domain) and 20.5 F1 score on FewRel 2.0 (cross-domain)
arXiv Detail & Related papers (2023-06-23T06:15:54Z) - DORE: Document Ordered Relation Extraction based on Generative Framework [56.537386636819626]
This paper investigates the root cause of the underwhelming performance of the existing generative DocRE models.
We propose to generate a symbolic and ordered sequence from the relation matrix which is deterministic and easier for model to learn.
Experimental results on four datasets show that our proposed method can improve the performance of the generative DocRE models.
arXiv Detail & Related papers (2022-10-28T11:18:10Z) - Learning Diverse Document Representations with Deep Query Interactions
for Dense Retrieval [79.37614949970013]
We propose a new dense retrieval model which learns diverse document representations with deep query interactions.
Our model encodes each document with a set of generated pseudo-queries to get query-informed, multi-view document representations.
arXiv Detail & Related papers (2022-08-08T16:00:55Z) - Does Recommend-Revise Produce Reliable Annotations? An Analysis on
Missing Instances in DocRED [60.39125850987604]
We show that a textit-revise scheme results in false negative samples and an obvious bias towards popular entities and relations.
The relabeled dataset is released to serve as a more reliable test set of document RE models.
arXiv Detail & Related papers (2022-04-17T11:29:01Z) - A sequence-to-sequence approach for document-level relation extraction [4.906513405712846]
Document-level relation extraction (DocRE) requires integrating information within and across sentences.
Seq2rel can learn the subtasks of DocRE end-to-end, replacing a pipeline of task-specific components.
arXiv Detail & Related papers (2022-04-03T16:03:19Z) - Eider: Evidence-enhanced Document-level Relation Extraction [56.71004595444816]
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.
arXiv Detail & Related papers (2021-06-16T09:43:16Z) - Document-Level Relation Extraction with Adaptive Thresholding and
Localized Context Pooling [34.93480801598084]
One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multiple possible relations.
We propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems.
arXiv Detail & Related papers (2020-10-21T20:41:23Z)
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.