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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Proceedings ArticleDOI
Question Answering Infused Pre-training of General-Purpose Contextualized Representations
TL;DR: The authors propose a pre-training objective based on question answering for learning general-purpose contextual representations, motivated by the intuition that the representation of a phrase in a passage should encode all questions that the phrase can answer in context.
Proceedings ArticleDOI
Automatically Generating Questions about Novel Metaphors in Literature
Natalie Parde,Rodney D. Nielsen +1 more
TL;DR: An approach that generates appropriate Questioning the Author queries based on novel metaphors in diverse syntactic relations in literature is developed, showing that the generated questions are comparable to human-generated questions in terms of naturalness, sensibility, and depth, and score slightly higher than human- generated questions in Terms of clarity.
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Entity Guided Question Generation with Contextual Structure and Sequence Information Capturing
TL;DR: Zhang et al. as mentioned in this paper proposed an entity guided question generation model with contextual structure information and sequence information capturing, which can generate comparable questions with state-of-the-art models.
Proceedings ArticleDOI
Video Question Generation via Semantic Rich Cross-Modal Self-Attention Networks Learning
TL;DR: A novel semantic rich cross-modal self-attention (SR-CMSA) network is proposed to aggregate the multi- modal and diverse features of a Video QG model to enhance the video frames semantic by integrating the object-level information and jointly consider the cross-Modal attention for the video question generation task.
Posted Content
Revisiting Paraphrase Question Generator using Pairwise Discriminator.
TL;DR: The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets and is shown to be statistically significant.
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