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google/mcic-coco
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MCIC-COCO is a machine comprehension dataset that is generated based on the publicly available COCO dataset. The technique to create such a dataset is reported in the paper: "Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task", Nan Ding, Sebastian Goodnman, Fei Sha, Radu Soricut We generate a datasets of over half-million examples, on which we estimate that the human-level accuracy is in the 83% range (in a 5-way multi-choice setup; for comparison, a random-guess approach has 20% accuracy). A novel neural-network architecture that combines the representation power of recursive neural networks with the discriminative power of fully-connected multi-layered networks (see above cited paper) achieves the best result as of the date of the dataset publication: 60.8% on the test set. What is enclosed in this package is the MCIC-COCO dataset. Datasets needed: D1. COCO images (train_2014 and val_2014). [Image data available for download at: http://mscoco.org/dataset/#download]. D2. The MCIC-COCO dataset that comes with this package, see data/ How to read MCIC-COCO dataset: Each line of the MCIC-COCO dataset is one example, which contains the following fields: answer_[0-4]: 5 candidate captions (tokenized) for the image. All captions come from captions_train2014.json and captions_val2014.json of the COCO dataset. image: the image filename from train_2014 or val_2014 of the COCO images example_id: a unique string id for each example reference: the answer index of the true caption (0 to 4) Note: Due to Google open-source policy, we replaced three sensitive words from the original dataset to "j*llyfish", "f*cking", "s*upid".
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