  Community Question Answering (cQA) forums have become a popular medium for
soliciting direct answers to specific questions of users from experts or other
experienced users on a given topic. However, for a given question, users
sometimes have to sift through a large number of low-quality or irrelevant
answers to find out the answer which satisfies their information need. To
alleviate this, the problem of Answer Quality Prediction (AQP) aims to predict
the quality of an answer posted in response to a forum question. Current AQP
systems either learn models using - a) various hand-crafted features (HCF) or
b) use deep learning (DL) techniques which automatically learn the required
feature representations.
  In this paper, we propose a novel approach for AQP known as - "Deep Feature
Fusion Network (DFFN)" which leverages the advantages of both hand-crafted
features and deep learning based systems. Given a question-answer pair along
with its metadata, DFFN independently - a) learns deep features using a
Convolutional Neural Network (CNN) and b) computes hand-crafted features using
various external resources and then combines them using a deep neural network
trained to predict the final answer quality. DFFN achieves state-of-the-art
performance on the standard SemEval-2015 and SemEval-2016 benchmark datasets
and outperforms baseline approaches which individually employ either HCF or DL
based techniques alone.
