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• W2964173634 abstract "Temporal common sense has applications in AI tasks such as QA, multi-document summarization, and human-AI communication. We propose the task of sequencing -- given a jumbled set of aligned image-caption pairs that belong to a story, the task is to sort them such that the output sequence forms a coherent story. We present multiple approaches, via unary (position) and pairwise (order) predictions, and their ensemble-based combinations, achieving strong results on this task. We use both text-based and image-based features, which depict complementary improvements. Using qualitative examples, we demonstrate that our models have learnt interesting aspects of temporal common sense." @default.
• W2964173634 created "2019-07-30" @default.
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• W2964173634 date "2016-01-01" @default.
• W2964173634 modified "2023-10-16" @default.
• W2964173634 title "Sort Story: Sorting Jumbled Images and Captions into Stories" @default.
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• W2964173634 doi "https://doi.org/10.18653/v1/d16-1091" @default.
• W2964173634 hasPublicationYear "2016" @default.
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