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
Automatic question generation based on sentence structure analysis using machine learning approach
Miroslav Blšták,Viera Rozinajová +1 more
Book ChapterDOI
Towards Multilingual Neural 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.
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Reinforced Multi-task Approach for Multi-hop Question Generation
TL;DR: This paper proposed a question-aware reward function in a reinforcement learning framework to maximize the utilization of the supporting facts in a multi-hop question generation model. But their model is limited in their capacity to focus on more than one supporting fact.
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What BERT Sees: Cross-Modal Transfer for Visual Question Generation
TL;DR: Evaluated visual capabilities of BERT out-of-the-box are evaluated, indicating an innate capacity for BERT-gen to adapt to multi-modal data and text generation, even with few data available, avoiding expensive pre-training.
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Neural Duplicate Question Detection without Labeled Training Data
TL;DR: This work proposes two novel methods—weak supervision using the title and body of a question, and the automatic generation of duplicate questions—and shows that both can achieve improved performances even though they do not require any labeled data.
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