README.md

metadata

annotations_creators:
  - Jordan Painter, Diptesh Kanojia
language:
  - en
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
pretty_name: 'Utilising Weak Supervision to create S3D: A Sarcasm Annotated Dataset'
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - text-classification

Table of Contents

• Dataset Description

Utilising Weak Supervision to Create S3D: A Sarcasm Annotated Dataset

This is the repository for the S3D dataset published at EMNLP 2022. The dataset can help build sarcasm detection models.

S3D-v2 Summary

The S3D-v2 dataset is our silver standard dataset of 100,000 tweets labelled for sarcasm using weak supervision by a majority voting system of fine-tuned sarcasm detection models. The models used are our roberta-large-finetuned-SARC-combined-DS, bertweet-base-finetuned-SARC-DS and bertweet-base-finetuned-SARC-combined-DS models.

S3D contains 13016 tweets labelled as sarcastic, and 86904 tweets labelled as not being sarcastic.

Data Fields

• Text: The preprocessed tweet
• Label: A label to denote if a given tweet is sarcastic

Data Splits

• Train: 70,000
• Valid: 15,000
• Test: 15,000