A request for clarity over the End of Sequence token in the
  Self-Critical Sequence Training
Jia Cheng Hu Roberto Cavicchioli Alessandro Capotondi
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}}