@@ -316,6 +316,8 @@ considering that we used only ~7% of the trainings data for the LSTM.
* Another effect which does not show in the evaluation metric is that even though the scores of *Singlelabel* and *Multilabel* are quite similar,
we observed some difference when we looked at it manually. *Singlelabel* often produced more gramatically sound sentences, but not fitting to the objects in the image,
while *Multilabel* recognized the objects better, but had slightly more errors in sentence structure (for an example see [Example captionresult](#example-captionresult)).
* It has been shown that automatic metrics don't work so well for that particular task. For reference see
"Why we need new evaluation metrics for nlg" [PDF](https://arxiv.org/pdf/1707.06875.pdf) and "The price of debiasing automatic metrics in natural language evaluation" [PDF](https://arxiv.org/pdf/1807.02202.pdf)
