Papers › MultiSubs: A Large-scale Multimodal and Multilingual Dataset
MultiSubs: A Large-scale Multimodal and Multilingual Dataset
Josiah Wang, Pranava Madhyastha, Josiel Figueiredo, Chiraag Lala, Lucia Specia
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 set up a fill-in-the-blank game for humans to evaluate the quality of the automatic image selection process of our dataset. We show the utility of the dataset on two automatic tasks: (i) fill-in-the-blank; (ii) lexical translation. Results of the human evaluation and automatic models demonstrate that images can be a useful complement to the textual context. 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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multimodal Lexical Translation | MultiSubs English-French | Multimodal BRNN | ALI | 0.81 | #1 of 1 | Archive leaderboard | report |
| Multimodal Lexical Translation | MultiSubs English-German | Multimodal BRNN | ALI | 0.94 | #1 of 1 | Archive leaderboard | report |
| Multimodal Lexical Translation | MultiSubs English-Portuguese | Multimodal BRNN | ALI | 0.80 | #1 of 1 | Archive leaderboard | report |
| Multimodal Lexical Translation | MultiSubs English-Spanish | Multimodal BRNN | ALI | 0.81 | #1 of 1 | Archive leaderboard | report |
| Multimodal Text Prediction | MultiSubs | 9-gram LM with back-off | Accuracy | 30.35 | #1 of 1 | Archive leaderboard | report |
| Multimodal Text Prediction | MultiSubs | 9-gram LM with back-off | Word similarity | 0.44 | #1 of 1 | Archive leaderboard | report |
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