title: The MERIT Dataset: Modelling and Efficiently Rendering Interpretable
    Transcripts
summary: This paper introduces the MERIT Dataset, a multimodal (text + image + layout)
  fully labeled dataset within the context of school reports. Comprising over 400
  labels and 33k samples, the MERIT Dataset is a valuable resource for training
  models in demanding Visually-rich Document Understanding (VrDU) tasks. By its
  nature (student grade reports), the MERIT Dataset can potentially include
  biases in a controlled way, making it a valuable tool to benchmark biases
  induced in Language Models (LLMs). The paper outlines the dataset's generation
  pipeline and highlights its main features in the textual, visual, layout, and
  bias domains. To demonstrate the dataset's utility, we present a benchmark with
  token classification models, showing that the dataset poses a significant
  challenge even for SOTA models and that these would greatly benefit from
  including samples from the MERIT Dataset in their pretraining phase.
paper_authors._id: 66d817fcde54fea8a03973f0
paper_authors.name: I. de Rodrigo
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-09-04T08:19:10.803Z
paper_authors._id: 63ee535a190ddd6214f30dc2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1676563169736-noauth.jpeg
paper_authors.fullname: de Rodrigo
paper_authors.type: user
paper_authors.user: de-Rodrigo
paper_authors._id: 66d817fcde54fea8a03973f1
paper_authors.name: A. Sanchez-Cuadrado
paper_authors._id: 66d817fcde54fea8a03973f2
paper_authors.name: J. Boal
paper_authors._id: 66d817fcde54fea8a03973f3
paper_authors.name: A. J. Lopez-Lopez
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-09-04T08:19:10.803Z
paper_authors._id: 645c966858f9ee315142a2e5
paper_authors.avatarUrl: /avatars/51928db15dddc07a51cc35a402f511c7.svg
paper_authors.fullname: Alvaro Lopez
paper_authors.type: user
paper_authors.user: alvlopez
paper_title: The MERIT Dataset: Modelling and Efficiently Rendering Interpretable
    Transcripts
paper_summary: This paper introduces the MERIT Dataset, a multimodal (text + image + layout)
  fully labeled dataset within the context of school reports. Comprising over 400
  labels and 33k samples, the MERIT Dataset is a valuable resource for training
  models in demanding Visually-rich Document Understanding (VrDU) tasks. By its
  nature (student grade reports), the MERIT Dataset can potentially include
  biases in a controlled way, making it a valuable tool to benchmark biases
  induced in Language Models (LLMs). The paper outlines the dataset's generation
  pipeline and highlights its main features in the textual, visual, layout, and
  bias domains. To demonstrate the dataset's utility, we present a benchmark with
  token classification models, showing that the dataset poses a significant
  challenge even for SOTA models and that these would greatly benefit from
  including samples from the MERIT Dataset in their pretraining phase.