  This study presents our approach on the automatic Vietnamese image captioning
for healthcare domain in text processing tasks of Vietnamese Language and
Speech Processing (VLSP) Challenge 2021, as shown in Figure 1. In recent years,
image captioning often employs a convolutional neural network-based
architecture as an encoder and a long short-term memory (LSTM) as a decoder to
generate sentences. These models perform remarkably well in different datasets.
Our proposed model also has an encoder and a decoder, but we instead use a Swin
Transformer in the encoder, and a LSTM combined with an attention module in the
decoder. The study presents our training experiments and techniques used during
the competition. Our model achieves a BLEU4 score of 0.293 on the vietCap4H
dataset, and the score is ranked the 3$^{rd}$ place on the private leaderboard.
Our code can be found at \url{https://git.io/JDdJm}.
