Effective shared representations with Multitask Learning for Community Question Answering
Daniele Bonadiman, Antonio Emmanuele Uva, Alessandro Moschitti · 2017
An important asset of using Deep Neural Networks (DNNs) for text applications is their ability to automatically engineer features.Unfortunately, DNNs usually require a lot of training data, especially for high-level semantic tasks such as community Question Answering (cQA).In this paper, we tackle the problem of data scarcity by learning the target DNN together with two auxiliary tasks in a multitask learning setting.We exploit the strong semantic connection between selection of comments relevant to (i) new questions and (ii) forum questions.This enables a global representation for comments, new and previous questions.The experiments of our model on a SemEval challenge dataset for cQA show a 20% relative improvement over standard DNNs.