  Multimodal learning on video and text data has been receiving growing
attention from many researchers in various research tasks, including
text-to-video retrieval, video-to-text retrieval, and video captioning.
Although many algorithms have been proposed for those challenging tasks, most
of them are developed on English language datasets. Despite Indonesian being
one of the most spoken languages in the world, the research progress on the
multimodal video-text with Indonesian sentences is still under-explored, likely
due to the absence of the public benchmark dataset. To address this issue, we
construct the first public Indonesian video-text dataset by translating English
sentences from the MSVD dataset to Indonesian sentences. Using our dataset, we
then train neural network models which were developed for the English
video-text dataset on three tasks, i.e., text-to-video retrieval, video-to-text
retrieval, and video captioning. The recent neural network-based approaches to
video-text tasks often utilized a feature extractor that is primarily
pretrained on an English vision-language dataset. Since the availability of the
pretraining resources with Indonesian sentences is relatively limited, the
applicability of those approaches to our dataset is still questionable. To
overcome the lack of pretraining resources, we apply cross-lingual transfer
learning by utilizing the feature extractors pretrained on the English dataset,
and we then fine-tune the models on our Indonesian dataset. Our experimental
results show that this approach can help to improve the performance for the
three tasks on all metrics. Finally, we discuss potential future works using
our dataset, inspiring further research in the Indonesian multimodal video-text
tasks. We believe that our dataset and our experimental results could provide
valuable contributions to the community. Our dataset is available on GitHub.
