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Contextual sequence mapping is one of the fundamental problems in Natural Language Processing. Instead of relying solely on the information presented in a text, the learning agents have access to a strong external signal given to assist the learning process. In this paper, we propose a novel family of Recurrent Neural Network unit: the Context-dependent Additive Recurrent Neural Network (CARNN) that is designed specifically to leverage this external signal. The experimental results on publicdoi:10.18653/v1/n18-1115 dblp:conf/naacl/TranLHZBB18 fatcat:7q2qgaecofhetdznmlaui3awd4