  The system we used for Task 6 (Automated Audio Captioning)of the Detection
and Classification of Acoustic Scenes and Events(DCASE) 2020 Challenge combines
three elements, namely, dataaugmentation, multi-task learning, and
post-processing, for audiocaptioning. The system received the highest
evaluation scores, butwhich of the individual elements most fully contributed
to its perfor-mance has not yet been clarified. Here, to asses their
contributions,we first conducted an element-wise ablation study on our systemto
estimate to what extent each element is effective. We then con-ducted a
detailed module-wise ablation study to further clarify thekey processing
modules for improving accuracy. The results showthat data augmentation and
post-processing significantly improvethe score in our system. In particular,
mix-up data augmentationand beam search in post-processing improve SPIDEr by
0.8 and 1.6points, respectively.
