Exploring the Ideal Depth of Neural Network when Predicting Question Deletion on Community Question Answering

by   Souvick Ghosh, et al.

In recent years, Community Question Answering (CQA) has emerged as a popular platform for knowledge curation and archival. An interesting aspect of question answering is that it combines aspects from natural language processing, information retrieval, and machine learning. In this paper, we have explored how the depth of the neural network influences the accuracy of prediction of deleted questions in question-answering forums. We have used different shallow and deep models for prediction and analyzed the relationships between number of hidden layers, accuracy, and computational time. The results suggest that while deep networks perform better than shallow networks in modeling complex non-linear functions, increasing the depth may not always produce desired results. We observe that the performance of the deep neural network suffers significantly due to vanishing gradients when large number of hidden layers are present. Constantly increasing the depth of the model increases accuracy initially, after which the accuracy plateaus, and finally drops. Adding each layer is also expensive in terms of the time required to train the model. This research is situated in the domain of neural information retrieval and contributes towards building a theory on how deep neural networks can be efficiently and accurately used for predicting question deletion. We predict deleted questions with more than 90% accuracy using two to ten hidden layers, with less accurate results for shallower and deeper architectures.


page 1

page 2

page 3

page 4


Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey

Text-based Question Answering (QA) is a challenging task which aims at f...

Improving Retrieval-Based Question Answering with Deep Inference Models

Question answering is one of the most important and difficult applicatio...

Studio Ousia's Quiz Bowl Question Answering System

In this chapter, we describe our question answering system, which was th...

Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning

The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve ...

Learning Representations and Agents for Information Retrieval

A goal shared by artificial intelligence and information retrieval is to...

DeepE: a deep neural network for knowledge graph embedding

Recently, neural network based methods have shown their power in learnin...

TRAC: Trustworthy Retrieval Augmented Chatbot

Although conversational AIs have demonstrated fantastic performance, the...

Please sign up or login with your details

Forgot password? Click here to reset