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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Computational Natural Language Inference: Robust and Interpretable Question Answering
TL;DR: A chronology of key events and events leading up to and including the publication of “The Godfather” is recounted.
Proceedings Article
Iterative GNN-based Decoder for Question Generation.
Zichu Fei,Qi Zhang,Yaqian Zhou +2 more
TL;DR: The authors design an Iterative Graph Network-based Decoder (IGND) to model the previous generation using a Graph Neural Network at each decoding step. But they do not consider the impact of copied words on the passage.
Book ChapterDOI
Semantic Templates for Generating Long-Form Technical Questions
Samiran Pal,Avinash Kumar Singh,Soham Datta,Sangameshwar Patil,Indrajit Bhattacharya,Girish Keshav Palshikar +5 more
TL;DR: This article proposed to generate technical questions using semantic templates and ensure that a large fraction of the generated questions are long-form, i.e., they require longer answers spanning multiple sentences.
Posted Content
WeaQA: Weak Supervision via Captions for Visual Question Answering
TL;DR: In this paper, a weakly-supervised approach is proposed to train models with synthetic Q-A pairs generated procedurally from captions, which can be trained without any human-annotated pairs, but with images and their associated textual descriptions or captions.
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