In this paper,we focus on the problem of answer triggering ad-dressed by Yang et al.(2015),which is a critical component for a real-world question answering system.We employ a hierarchical gated recurrent neural tensor(HGRNT)model to capture both the context information and the deep in-teractions between the candidate answers and the question.Our result on F val-ue achieves 42.6%,which surpasses the baseline by over 10%.