Non Factoid Question Category classification

This repository contains code for the following paper:

"A Non-Factoid Question-Answering Taxonomy" published at SIGIR '22, won "Best Paper" Award
Valeriia Bolotova, Vladislav Blinov, W. Falk Scholer, Bruce Croft, Mark Sanderson ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022

NF_CATS Dataset

The dataset for training is located in nfcats/data.

Model

The trained model could be downloaded from the hugginface repository and you can test the model via hugginface space.

Installation

From source:

cd NF-CATS
pip install poetry>=1.0.5
poetry install

Usage

Run test validation of best fine-tuned model:

python nfcats/predict.py

Train transformer model

python nfcats/train.py

Tf-idf experiments:

python tf_idf.py 

Citation

If you use NFQA-cats in your work, please cite this paper

@misc{bolotova2022nfcats,
        author = {Bolotova, Valeriia and Blinov, Vladislav and Scholer, Falk and Croft, W. Bruce and Sanderson, Mark},
        title = {A Non-Factoid Question-Answering Taxonomy},
        year = {2022},
        isbn = {9781450387323},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        url = {https://doi.org/10.1145/3477495.3531926},
        doi = {10.1145/3477495.3531926},
        booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
        pages = {1196–1207},
        numpages = {12},
        keywords = {question taxonomy, non-factoid question-answering, editorial study, dataset analysis},
        location = {Madrid, Spain},
        series = {SIGIR '22}
}