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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
To achieve the long-term goal of machines being able to engage humans in conversation, our models should captivate the interest of their speaking partners. Communication grounded in images, whereby a dialogue is conducted based on a given photo, is a setup naturally appealing to humans (Hu et al., 2014) . In this work we study large-scale architectures and datasets for this goal. We test a set of neural architectures using state-of-the-art image and text representations, considering variousdoi:10.18653/v1/2020.acl-main.219 fatcat:y3t3d7ojzzf5pg54g5td5ko7c4