Related papers: A request for clarity over the End of Sequence token in the Self-Critical Sequence Training

A request for clarity over the End of Sequence token in the Self-Critical Sequence Training

URL: http://arxiv.org/abs/2305.12254v1
Date: Sat, 20 May 2023 18:01:47 GMT
Title: A request for clarity over the End of Sequence token in the Self-Critical Sequence Training
Authors: Jia Cheng Hu, Roberto Cavicchioli and Alessandro Capotondi
Abstract summary: This work proposes to solve the problem by spreading awareness of the issue itself. In particular, we invite future works to share a simple and informative signature with the help of a library called SacreEOS.
Score: 69.3939291118954
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: The Image Captioning research field is currently compromised by the lack of transparency and awareness over the End-of-Sequence token (<Eos>) in the Self-Critical Sequence Training. If the <Eos> token is omitted, a model can boost its performance up to +4.1 CIDEr-D using trivial sentence fragments. While this phenomenon poses an obstacle to a fair evaluation and comparison of established works, people involved in new projects are given the arduous choice between lower scores and unsatisfactory descriptions due to the competitive nature of the research. This work proposes to solve the problem by spreading awareness of the issue itself. In particular, we invite future works to share a simple and informative signature with the help of a library called SacreEOS. Code available at \emph{\href{https://github.com/jchenghu/sacreeos}{https://github.com/jchenghu/sacreeos}}

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