# Deep Feature Fusion Network for Answer Quality Prediction in Community Question Answering Source: [Deep Feature Fusion Network for Answer Quality Prediction in Community Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:23:26.300Z Authors: Sai Praneeth Suggu, Kushwanth N. Goutham, Manoj K. Chinnakotla, Manish Shrivastava Published: 2016-06-22T20:58:08.000Z ## 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) 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()