Hand in Glove: Deep Feature Fusion Network Architectures for Answer Quality Prediction in Community Question Answering
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) 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 combines the advantages of both hand crafted features and deep learning based systems. Given a question answer pair along with its metadata, the DFFN architecture independently a) learns features from the Deep Neural Network (DNN) and b) computes hand crafted features using various external resources and then combines them using a fully connected neural network trained to predict the final answer quality. DFFN is end end differentiable and trained as a single system. We propose two different DFFN architectures which vary mainly in the way they model the input question/answer pair DFFN CNN uses a Convolutional Neural Network (CNN) and DFFN BLNA uses a Bi directional LSTM with Neural Attention (BLNA) Both these proposed variants of DFFN (DFFN CNN and DFFN BLNA) achieve 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.
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