  Answer triggering is the task of selecting the best-suited answer for a given
question from a set of candidate answers if exists. In this paper, we present a
hybrid deep learning model for answer triggering, which combines several
dependency graph based alignment features, namely graph edit distance,
graph-based similarity and dependency graph coverage, with dense vector
embeddings from a Convolutional Neural Network (CNN). Our experiments on the
WikiQA dataset show that such a combination can more accurately trigger a
candidate answer compared to the previous state-of-the-art models. Comparative
study on WikiQA dataset shows 5.86% absolute F-score improvement at the
question level.
