MultiSubs: A Large-scale Multimodal and Multilingual Dataset

Abstract

This paper introduces a large-scale multimodal and multilingual dataset that aims to facilitate research on grounding words to images in their contextual usage in language. The dataset consists of images selected to unambiguously illustrate concepts expressed in sentences from movie subtitles. The dataset is a valuable resource as (i) the images are aligned to text fragments rather than whole sentences; (ii) multiple images are possible for a text fragment and a sentence; (iii) the sentences are free-form and real-world like; (iv) the parallel texts are multilingual. We also set up a fill-in-the-blank game for humans to evaluate the quality of the automatic image selection process of our dataset. Finally, we propose a fill-in-the-blank task to demonstrate the utility of the dataset, and present some baseline prediction models. The dataset will benefit research on visual grounding of words especially in the context of free-form sentences, and can be obtained from https://doi.org/10.5281/zenodo.5034604 under a Creative Commons licence.