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

Investigating the use of Paraphrase Generation for Question Reformulation in the FRANK QA system