Combining Graph-based Dependency Features with Convolutional Neural Network for Answer Triggering

08/05/2018
by   Deepak Gupta, et al.
0

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 question level.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset