Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
Table 1. Evaluation results of different models on SQuAD dataset.
['[BOLD] Model', '[BOLD] B1', '[BOLD] B2', '[BOLD] B3', '[BOLD] B4', '[BOLD] ROUGE-L', '[BOLD] METEOR']
Main Results. Table. We can see that our CS2S-VR-ACS achieves the best performance in terms of the evaluation metrics and outperforms baselines by a great margin. Comparing CS2S-VR-A with Seq2Seq-based answer-aware QG baselines, we can see that it outperforms all the baseline approaches in that category with the same input information (input passage and answer span). This is because that our content separation strategy and vocabulary reduction operation help the model to better learn what words to copy from the inputs. Comparing our ACS-aware QG models and variants with auxiliary-information-enhanced models (such as NQG-Knowledge and DLPH) and auxiliary-task-enhanced baselines (such as BERT-QG-QAP), we can see that the clue and style information helps to generate better results than models with knowledge or difficulty information. That is because our ACS-aware setting makes the question generation problem closer to one-to-one mapping, and greatly reduces the task difficulty.