You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions
Tasnim Kabir, Yoo Yeon Sung, Saptarashmi Bandyopadhyay, Hao Zou, Abhranil Chandra, Jordan Lee Boyd-Graber
Abstract
Training question-answering QA and information retrieval systems for web queries require large, expensive datasets that are difficult to annotate and time-consuming to gather. Moreover, while natural datasets of information-seeking questions are often prone to ambiguity or ill-formed, there are troves of freely available, carefully crafted question datasets for many languages. Thus, we automatically generate shorter, information-seeking questions, resembling web queries in the style of the Natural Questions (NQ) dataset from longer trivia data. Training a QA system on these transformed questions is a viable strategy for alternating to more expensive training setups showing the F1 score difference of less than six points and contrasting the final systems.
BibTeX
@inproceedings{kabir-etal-2024-make,
title = "You Make me Feel like a Natural Question: Training {QA} Systems on Transformed Trivia Questions",
author = "Kabir, Tasnim and
Sung, Yoo Yeon and
Bandyopadhyay, Saptarashmi and
Zou, Hao and
Chandra, Abhranil and
Boyd-Graber, Jordan Lee",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1140/",
doi = "10.18653/v1/2024.emnlp-main.1140",
pages = "20486--20510"
}