NAACL 2021long5 citations
EaSe: A Diagnostic Tool for VQA based on Answer Diversity
Shailza Jolly, Sandro Pezzelle, Moin Nabi
Abstract
We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample. EASE is based on the pattern of answers provided by multiple annotators to a given question. In particular, it considers two aspects of the answers: (i) their Entropy; (ii) their Semantic content. First, we prove the validity of our diagnostic to identify samples that are easy/hard for state-of-art VQA models. Second, we show that EASE can be successfully used to select the most-informative samples for training/fine-tuning. Crucially, only information that is readily available in any VQA dataset is used to compute its scores.
BibTeX
@inproceedings{jolly-etal-2021-ease,
title = "{E}a{S}e: A Diagnostic Tool for {VQA} based on Answer Diversity",
author = "Jolly, Shailza and
Pezzelle, Sandro and
Nabi, Moin",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.192/",
doi = "10.18653/v1/2021.naacl-main.192",
pages = "2407--2414"
}