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For more\neffective annotation acquisition, we develop a recommendation-assisted\ncollective annotation system, automatically providing an annotator with several\ntags and sentences deemed to be relevant with respect to the pictorial content.\nHaving 20,342 images annotated with 27,218 Chinese sentences and 70,993 tags,\nCOCO-CN is currently the largest Chinese-English dataset that provides a\nunified and challenging platform for cross-lingual image tagging, captioning\nand retrieval. We develop conceptually simple yet effective methods per task\nfor learning from cross-lingual resources. Extensive experiments on the three\ntasks justify the viability of the proposed dataset and methods. 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