  We present a study into the ability of paraphrase generation methods to
increase the variety of natural language questions that the FRANK Question
Answering system can answer. We first evaluate paraphrase generation methods on
the LC-QuAD 2.0 dataset using both automatic metrics and human judgement, and
discuss their correlation. Error analysis on the dataset is also performed
using both automatic and manual approaches, and we discuss how paraphrase
generation and evaluation is affected by data points which contain error. We
then simulate an implementation of the best performing paraphrase generation
method (an English-French backtranslation) into FRANK in order to test our
original hypothesis, using a small challenge dataset. Our two main conclusions
are that cleaning of LC-QuAD 2.0 is required as the errors present can affect
evaluation; and that, due to limitations of FRANK's parser, paraphrase
generation is not a method which we can rely on to improve the variety of
natural language questions that FRANK can answer.
