Yansheng Cao


CMU-MOSEAS: A Multimodal Language Dataset for Spanish, Portuguese, German and French
AmirAli Bagher Zadeh | Yansheng Cao | Simon Hessner | Paul Pu Liang | Soujanya Poria | Louis-Philippe Morency
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Modeling multimodal language is a core research area in natural language processing. While languages such as English have relatively large multimodal language resources, other widely spoken languages across the globe have few or no large-scale datasets in this area. This disproportionately affects native speakers of languages other than English. As a step towards building more equitable and inclusive multimodal systems, we introduce the first large-scale multimodal language dataset for Spanish, Portuguese, German and French. The proposed dataset, called CMU-MOSEAS (CMU Multimodal Opinion Sentiment, Emotions and Attributes), is the largest of its kind with 40,000 total labelled sentences. It covers a diverse set topics and speakers, and carries supervision of 20 labels including sentiment (and subjectivity), emotions, and attributes. Our evaluations on a state-of-the-art multimodal model demonstrates that CMU-MOSEAS enables further research for multilingual studies in multimodal language.