MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification
This dataset addresses the problem of classifying biomedical experiment methods for researchers and AI developers, providing a new resource for multimodal learning in scientific domains.
The authors introduce MELINDA, a new multimodal dataset for biomedical experiment method classification, collected via automated distant supervision. Benchmarking state-of-the-art NLP and computer vision models, they find that multimodal models outperform unimodal ones, but still require improvements in visual concept grounding and transferability to low-resource domains.
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.