README.md

metadata

annotations_creators: []
language: []
language_creators:
  - found
license: []
multilinguality:
  - monolingual
pretty_name: Deep Research USC
size_categories:
  - 10B<n<100B
source_datasets: []
tags:
  - continual learning
task_categories: []
task_ids: []

Dataset Card for "deep-research"

Table of Contents

• Table of Contents
• Dataset Description
• Dataset Summary
• Supported Tasks and Leaderboards
• Languages
• Dataset Structure
• Data Instances
• Data Fields
• Data Splits
• Dataset Creation
• Curation Rationale
• Source Data
  • Initial Data Collection and Normalization
  • Who are the source language producers?
• Annotations
  • Annotation process
  • Who are the annotators?
• Personal and Sensitive Information
• Considerations for Using the Data
• Social Impact of Dataset
• Discussion of Biases
• Other Known Limitations
• Additional Information
• Dataset Curators
• Licensing Information
• Citation Information
• Contributions

Dataset Description

• Homepage: Deep USC Research
• Repository: More Information Needed
• Paper: Multimodal Phased Transformer for Sentiment Analysis
• Point of Contact: Iordanis Fostiropoulos

Dataset Summary

Briefly summarize the dataset...

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances.

train.json

• Size of downloaded dataset files: 181.42 MB
• Size of the generated dataset: 522.66 MB
• Total amount of disk used: 704.07 MB

An example of 'train' looks as follows.

This example was too long and was cropped:
{'id': '5733be284776f41900661182',
 'title': 'University_of_Notre_Dame',
 'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary...',
 'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
 'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}
 }

dev.json

• Size of downloaded dataset files: 183.09 MB
• Size of the generated dataset: 523.97 MB
• Total amount of disk used: 707.06 MB

An example of 'devepopment' looks as follows.

This example was too long and was cropped:

{'id': '5733be284776f41900661182',
 'title': 'University_of_Notre_Dame',
 'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary...',
 'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
 'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}
 }

Data Fields

• id: ID of the context, question unit
• title: Title of the question ...

Data Splits

	train	development	test
Input Sentences
Average Sentence Length

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed

Annotations

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

More Information Needed

Additional Information

Licensing Information

More Information Needed

Citation Information

Provide the BibTex-formatted reference for the dataset. For example:

@inproceedings{cheng-etal-2021-multimodal,
    title = "Multimodal Phased Transformer for Sentiment Analysis",
    author = "Cheng, Junyan  and
      Fostiropoulos, Iordanis  and
      Boehm, Barry  and
      Soleymani, Mohammad",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.189",
    doi = "10.18653/v1/2021.emnlp-main.189",
    pages = "2447--2458",
    abstract = "Multimodal Transformers achieve superior performance in multimodal learning tasks. However, the quadratic complexity of the self-attention mechanism in Transformers limits their deployment in low-resource devices and makes their inference and training computationally expensive. We propose multimodal Sparse Phased Transformer (SPT) to alleviate the problem of self-attention complexity and memory footprint. SPT uses a sampling function to generate a sparse attention matrix and compress a long sequence to a shorter sequence of hidden states. SPT concurrently captures interactions between the hidden states of different modalities at every layer. To further improve the efficiency of our method, we use Layer-wise parameter sharing and Factorized Co-Attention that share parameters between Cross Attention Blocks, with minimal impact on task performance. We evaluate our model with three sentiment analysis datasets and achieve comparable or superior performance compared with the existing methods, with a 90{\%} reduction in the number of parameters. We conclude that (SPT) along with parameter sharing can capture multimodal interactions with reduced model size and improved sample efficiency.",
}

Contributions

Thanks to @github-username for adding this dataset.