Title: Improving on Q & A Recurrent Neural Networks Using Noun-Tagging,
Abstract: Often, more time is spent on finding a model that works well, rather than
tuning the model and working directly with the dataset. Our research began as
an attempt to improve upon a simple Recurrent Neural Network for answering
"simple" first-order questions (QA-RNN), developed by Ferhan Ture and Oliver
Jojic, from Comcast Labs, using the SimpleQuestions dataset. Their baseline
model, a bidirectional, 2-layer LSTM RNN and a GRU RNN, have accuracies of 0.94
and 0.90, for entity detection and relation prediction, respectively. We fine
tuned these models by doing substantial hyper-parameter tuning, getting
resulting accuracies of 0.70 and 0.80, for entity detection and relation
prediction, respectively. An accuracy of 0.984 was obtained on entity detection
using a 1-layer LSTM, where preprocessing was done by removing all words not
part of a noun chunk from the question. 100% of the dataset was available for
relation prediction, but only 20% of the dataset, was available for entity
detection, which we believe to be much of the reason for our initial
difficulties in replicating their result, despite the fact we were able to
improve on their entity detection results.