Abstract
Extracting relations in social media posts is challenging when sentences lack of contexts. However, images related to these sentences can supplement such missing contexts and help to identify relations precisely. To this end, we present a multimodal neural relation extraction dataset (MNRE), consisting of 10000+ sentences on 31 relations derived from Twitter and annotated by crowdworkers. The subject and object entities are recognized by a pretrained NER tool and then filtered by crowdworkers. All the relations are identified manually. One sentence is tagged with one related image. We develop a multimodal relation extraction baseline model and the experimental results show that introducing multimodal information improves relation extraction performance in social media texts. Still, our detailed analysis points out the difficulties of aligning relations in texts and images, which can be addressed for future research. All details and resources about the dataset and baselines are released on https://github.com/thecharm/MNRE.
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