XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser

• URL: http://arxiv.org/abs/2405.17336v1
• Date: Mon, 27 May 2024 16:37:17 GMT
• Title: XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser
• Authors: Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Kui Wu, Fei Liu, Wei Zhang, Tao Sun, Tongliang Li, Zhoujun Li,
• Abstract summary: In this work, we introduce a simple but effective textbfMultimodal and textbfMultilingual semi-structured textbfFORM textbfXForm framework. textbfXForm is anchored on a comprehensive pre-trained language model and innovatively amalgamates entity recognition and relationRE. Our framework exhibits exceptionally improved performance across tasks in both multi-language and zero-shot contexts.
• Score: 35.69888780388425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the domain of document AI, semi-structured form parsing plays a crucial role. This task leverages techniques from key information extraction (KIE), dealing with inputs that range from plain text to intricate modal data comprising images and structural layouts. The advent of pre-trained multimodal models has driven the extraction of key information from form documents in different formats such as PDFs and images. Nonetheless, the endeavor of form parsing is still encumbered by notable challenges like subpar capabilities in multi-lingual parsing and diminished recall in contexts rich in text and visuals. In this work, we introduce a simple but effective \textbf{M}ultimodal and \textbf{M}ultilingual semi-structured \textbf{FORM} \textbf{PARSER} (\textbf{XFormParser}), which is anchored on a comprehensive pre-trained language model and innovatively amalgamates semantic entity recognition (SER) and relation extraction (RE) into a unified framework, enhanced by a novel staged warm-up training approach that employs soft labels to significantly refine form parsing accuracy without amplifying inference overhead. Furthermore, we have developed a groundbreaking benchmark dataset, named InDFormBench, catering specifically to the parsing requirements of multilingual forms in various industrial contexts. Through rigorous testing on established multilingual benchmarks and InDFormBench, XFormParser has demonstrated its unparalleled efficacy, notably surpassing the state-of-the-art (SOTA) models in RE tasks within language-specific setups by achieving an F1 score improvement of up to 1.79\%. Our framework exhibits exceptionally improved performance across tasks in both multi-language and zero-shot contexts when compared to existing SOTA benchmarks. The code is publicly available at https://github.com/zhbuaa0/layoutlmft.

Related papers

• SRFUND: A Multi-Granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding [55.48936731641802]
  We present the SRFUND, a hierarchically structured multi-task form understanding benchmark. SRFUND provides refined annotations on top of the original FUNSD and XFUND datasets. The dataset includes eight languages including English, Chinese, Japanese, German, French, Spanish, Italian, and Portuguese.
  arXiv  Detail & Related papers  (2024-06-13T02:35:55Z)
• MACT: Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing [4.536003573070846]
  We introduce a cross-lingual training strategy for semantic representation parsing models. It exploits the alignments between languages encoded in pre-trained language models. Experiments show significant improvements in DRS clause and graph parsing in English, German, Italian and Dutch.
  arXiv  Detail & Related papers  (2024-06-03T07:02:57Z)
• A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching [60.51839859852572]
  We propose to resolve the text into multi concepts for multilingual semantic matching to liberate the model from the reliance on NER models. We conduct comprehensive experiments on English datasets QQP and MRPC, and Chinese dataset Medical-SM.
  arXiv  Detail & Related papers  (2024-03-05T13:55:16Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages [105.54207724678767]
  Data scarcity is a crucial issue for the development of highly multilingual NLP systems. We propose XTREME-UP, a benchmark defined by its focus on the scarce-data scenario rather than zero-shot. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies.
  arXiv  Detail & Related papers  (2023-05-19T18:00:03Z)
• PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis [6.155943751502232]
  We present a language-independent graph neural network (GNN)-based model that achieves competitive results on common document layout datasets. Our model is suitable for industrial applications, particularly in multi-language scenarios.
  arXiv  Detail & Related papers  (2023-04-24T03:54:48Z)
• Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data [2.225882303328135]
  We propose a novel Translate-and-Fill (TaF) method to produce silver training data for a multilingual semantic parsing task. Experimental results on three multilingual semantic parsing datasets show that data augmentation with TaF reaches accuracies competitive with similar systems.
  arXiv  Detail & Related papers  (2021-09-09T14:51:11Z)
• mForms : Multimodal Form-Filling with Question Answering [1.7614751781649955]
  This paper presents a new approach to form-filling by reformulating the task as multimodal natural language Question Answering (QA) The reformulation is achieved by first translating the elements on the GUI form (text fields, buttons, icons, etc.) to natural language questions, where these questions capture the element's multimodal semantics. Results show the new approach not only maintains robust accuracy for sparse training conditions but achieves state-of-the-art F1 of 0.97 on ATIS with approximately 1/10th of the training data.
  arXiv  Detail & Related papers  (2020-11-24T19:47:53Z)
• Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model [58.27176041092891]
  Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements. We propose a novel unsupervised feature decomposition method that can automatically extract domain-specific features from the entangled pretrained cross-lingual representations. Our proposed model leverages mutual information estimation to decompose the representations computed by a cross-lingual model into domain-invariant and domain-specific parts.
  arXiv  Detail & Related papers  (2020-11-23T16:00:42Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)
• Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching [28.190001111358438]
  We propose a Siamese Multi-depth Transformer-based SMITH for long-form document matching. Our model contains several innovations to adapt self-attention models for longer text input. We will open source a Wikipedia based benchmark dataset, code and a pre-trained checkpoint to accelerate future research on long-form document matching.
  arXiv  Detail & Related papers  (2020-04-26T07:04:08Z)

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.