varuntotakura/AL_RCTA

An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations

Paper and repository

This repository accompanies a recently accepted paper and contains the cleaned datasets, collected annotator files, and scripts to reproduce the experimental splits used in the study.

• https://arxiv.org/abs/2604.23290

Datasets (summary)

We used three benchmark text-classification datasets:

• AG News (4 classes): world, sports, business, science/technology.
• Consumer Complaints (6 classes): debt collection, prepaid card/debit card, mortgage, checking/savings account, student loan, vehicle loan/lease. (Source: https://catalog.data.gov/dataset/consumer-complaint-database)
• Wikipedia Movie Plots (4 classes — movie genres): drama, comedy, horror, action. (Source: https://www.kaggle.com/datasets/jrobischon/wikipedia-movie-plots)

For each dataset we randomly sampled 3,000 examples and collected annotations via Upwork/Amazon Mechanical Turk from multiple distinct workers (10 annotators per sampled set, Wikipedia includes 9 annotator files in this repo). Annotators could abstain by entering label 0. The protocol received ethical review and participant consent; no worker identities were recorded.

Folder structure & key files

IJCNN_2026/

• Contains the Supplemental file for the IJCNN 2026 main paper

Data_AGNewsGroups/

• Cleaned_AG_News_Dataset_3_columns_ALL.xlsx — Main cleaned dataset (Index, Description, Class Index).
• Annotations/ — Human annotation files (AG_Upwork_.xlsx).

Data_ConsumerComplaints/

• Cleaned_Dataset_All.xlsx — Main cleaned dataset (Index, Consumer complaint narrative, Product).
• Annotations/ — Human annotation files (CC_Upwork_.xlsx).

Data_WikipediaMoviePlots/

• Cleaned_Dataset_All.csv — Main cleaned dataset (Index, Plot, Genre).
• Annotations/ — Human annotation files (Wiki_Upwork_.csv) — nine annotator files provided.

All dataset files in this repo use an "Index" column (unique id), a text column standardized as "Description", and a label column standardized as "Labels".

Directory structure

A concise view of the repository layout:

.
├─ README.md
├─ IJCNN Paper/
│  ├─ IJCNN_2026_Supplemental.pdf
├─ Data_AGNewsGroups/
│  ├─ Cleaned_AG_News_Dataset_3_columns_ALL.xlsx
│  └─ Annotations/
├─ Data_ConsumerComplaints/
│  ├─ Cleaned_Dataset_All.xlsx
│  └─ Annotations/
├─ Data_WikipediaMoviePlots/
│  ├─ Cleaned_Dataset_All.csv
│  └─ Annotations/
.

Use the provided cleaned files and annotation folders to reproduce the splits and experiments described in the paper. Scripts in the codebase expect the file names and columns described above.

Citation & contact

If you use this repository or the collected annotations, please cite the accompanying paper and acknowledge the dataset sources.

Paper citation (BibTeX)

The paper has been accepted to WCCI 2026 - IJCNN (to appear). Use the following BibTeX entry:

@InProceedings{al_rcta2026,
  author    = {Varun Totakura and Ankita Singh and Yushun Dong and Shayok Chakraborty},
  title     = {{An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations}},
  booktitle = {Proceedings of the IEEE World Congress on Computational Intelligence (WCCI) -- International Joint Conference on Neural Networks (IJCNN)},
  month     = {June},
  year      = {2026},
  note      = {Accepted for publication}
}

Creative Commons Attribution 4.0 International (CC BY 4.0)

Copyright (c) 2026 Varun Totakura

This work is licensed under the Creative Commons Attribution 4.0 International License.

You are free to:

• Share — copy and redistribute the material in any medium or format
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Full license text: https://creativecommons.org/licenses/by/4.0/