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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Extractive text summarisation using Bayesian state estimation of sentences: A Markovian framework
TL;DR: A sequential Markov model, equipped with Bayesian inference, is presented, to estimate the degree of importance of sentences in a document and thereby address the text summarisation problem.
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Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums
TL;DR: This paper takes the first step on teaching machines to ask open-answered questions from real-world news for open discussion (openQG), and proposes a model based on conditional generative adversarial networks and question evaluation model that can generate questions with higher quality compared with commonly-used text generation methods.
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Summary-Oriented Question Generation for Informational Queries.
TL;DR: This article used a BERT-based Pointer-Generator network trained on the Natural Questions (NQ) dataset to generate self-explanatory questions that focus on main document topics and are answerable with variable length passages as appropriate.
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AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents
TL;DR: The authors proposed a system that integrates the tasks of question answering (QA) and question generation (QG) in order to produce Q&A items that convey the content of multi-paragraph documents.
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Extended Answer and Uncertainty Aware Neural Question Generation
TL;DR: An Extended Answer-aware Network (EAN) is proposed which is trained with Word-based Coverage Mechanism and decodes with Uncertainty-aware Beam Search to seek a better balance between the model confidence in copying words from an input text paragraph and the confidence in generating words from a vocabulary.
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