  We introduce a new dataset, MELINDA, for Multimodal biomEdicaL experImeNt
methoD clAssification. The dataset is collected in a fully automated distant
supervision manner, where the labels are obtained from an existing curated
database, and the actual contents are extracted from papers associated with
each of the records in the database. We benchmark various state-of-the-art NLP
and computer vision models, including unimodal models which only take either
caption texts or images as inputs, and multimodal models. Extensive experiments
and analysis show that multimodal models, despite outperforming unimodal ones,
still need improvements especially on a less-supervised way of grounding visual
concepts with languages, and better transferability to low resource domains. We
release our dataset and the benchmarks to facilitate future research in
multimodal learning, especially to motivate targeted improvements for
applications in scientific domains.
