@InProceedings{suggu-EtAl:2016:COLING,
  author    = {Suggu, Sai Praneeth  and  Naga Goutham, Kushwanth  and  Chinnakotla, Manoj K.  and  Shrivastava, Manish},
  title     = {Hand in Glove: Deep Feature Fusion Network Architectures for Answer Quality Prediction in Community Question Answering},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1429--1440},
  abstract  = {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.},
  url       = {http://aclweb.org/anthology/C16-1135}
}