We evaluate our method against existing ones in terms of the quality of generated questions as well as the fine-tuned MRC model accuracy after training on the data synthetically generated by our method.
Ranked #4 on Question Generation on SQuAD1.1 (using extra training data)
Real-world question answering systems often retrieve potentially relevant documents to a given question through a keyword search, followed by a machine reading comprehension (MRC) step to find the exact answer from them.
We evaluate the question generation capability of our method by comparing the BLEU score with existing methods and test our method by fine-tuning the MRC model on the downstream MRC data after training on synthetic data.
In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks.
Ranked #4 on Question Answering on NarrativeQA
Machine reading comprehension helps machines learn to utilize most of the human knowledge written in the form of text.
Ranked #13 on Question Answering on TriviaQA
Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing.
Ranked #69 on Question Answering on SQuAD1.1