  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 %.
