  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.
