Learning to Ask: Neural Question Generation for Reading Comprehension
Xinya Du,Junru Shao,Claire Cardie +2 more
- Vol. 1, pp 1342-1352
TLDR
This paper proposed an attention-based sequence learning model for question generation from text passages in reading comprehension, which is trainable end-to-end via sequence-tosequence learning and significantly outperforms the state-of-the-art rule-based system.Abstract:
We study automatic question generation for sentences from text passages in reading comprehension. We introduce an attention-based sequence learning model for the task and investigate the effect of encoding sentence- vs. paragraph-level information. In contrast to all previous work, our model does not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead trainable end-to-end via sequence-to-sequence learning. Automatic evaluation results show that our system significantly outperforms the state-of-the-art rule-based system. In human evaluations, questions generated by our system are also rated as being more natural (i.e.,, grammaticality, fluency) and as more difficult to answer (in terms of syntactic and lexical divergence from the original text and reasoning needed to answer).read more
Citations
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Journal ArticleDOI
Towards End-to-End Multilingual Question Answering
TL;DR: It is demonstrated that a simple translation of texts can be inadequate in case of Arabic, English and German languages (on InsuranceQA and SemEval datasets), and thus more sophisticated models are required.
Journal ArticleDOI
A Review on Question Generation from Natural Language Text
TL;DR: In this article, the authors proposed a method to generate natural and relevant questions from various input formats, e.g., natural language, text, and images, using a question generation model.
Journal ArticleDOI
Neural Question Generation with Answer Pivot
TL;DR: This paper treats the answers as the hidden pivot for question generation and combines the questiongeneration and answer selection process in a joint model and achieves the state-of-the-art result on the SQuAD dataset according to automatic metric and human evaluation.
Proceedings ArticleDOI
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
TL;DR: The authors proposed a data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks.
Proceedings ArticleDOI
BERT for Question Generation.
Ying-Hong Chan,Yao-Chung Fan +1 more
TL;DR: Two neural architectures built on top of BERT for question generation tasks are introduced by restructuring the BERT employment into a sequential manner for taking information from previous decoded results.
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