QURIOUS: Question Generation Pretraining for Text Generation

Narayan, Shashi; Simões, Gonçalo; Ma, Jing; Craighead, Hannah; McDonald, Ryan

doi:10.48550/arxiv.2004.11026

“…QG has progressed rapidly due to new datasets and model improvements. Many different QG models have been proposed, starting for simple vanilla Sequence to Sequence Neural Networks models (seq2seq) Yuan et al, 2017) to the more recent transformer-based models (Dong et al, 2019;Chan and Fan, 2019;Varanasi et al, 2020;Narayan et al, 2020;Bao et al, 2020). Some QG systems use manual linguistic features in their models (Harrison and Walker, 2018;Khullar et al, 2018;Liu et al, 2019a;Dhole and Manning, 2020), some consider how to select question-worthy content Li et al, 2019;Scialom et al, 2019;, and some systems explicitly model question types (Duan et al, 2017;Sun et al, 2018;Kang et al, 2019;.…”

Section: Related Workmentioning

confidence: 99%

Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

Ghanem¹,

Lutz²,

Rivard³

et al. 2022

Findings of the Association for Computational Linguistics: ACL 2022

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Reading is integral to everyday life, and yet learning to read is a struggle for many young learners. During lessons, teachers can use comprehension questions to increase engagement, test reading skills, and improve retention. Historically such questions were written by skilled teachers, but recently language models have been used to generate comprehension questions. However, many existing Question Generation (QG) systems focus on generating literal questions from the text, and have no way to control the type of the generated question. In this paper, we study QG for reading comprehension where inferential questions are critical and extractive techniques cannot be used. We propose a two-step model (HTA-WTA) that takes advantage of previous datasets, and can generate questions for a specific targeted comprehension skill. We propose a new reading comprehension dataset that contains questions annotated with story-based reading comprehension skills (SBRCS), allowing for a more complete reader assessment. Across several experiments, our results show that HTA-WTA outperforms multiple strong baselines on this new dataset. We show that the HTA-WTA model tests for strong SCRS by asking deep inferential questions.

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“…2 Related Work 2.1 Question generation in NLP Question Generation (QG) is an active research topic in NLP. It is explored as a standalone task (Heilman and Smith, 2009;Nema et al, 2019), as a pre-training task for language models (Narayan et al, 2020) and as a component in solutions for other textual tasks, such as question answering Puri et al, 2020), information retrieval (Mass et al, 2020;Gaur et al, 2021) and generation evaluation (Durmus et al, 2020;Honovich et al, 2021). There are two main directions to QG: template-based (Heilman and Smith, 2009;Lyu et al, 2021;Dhole and Manning, 2020) and neural-based, with the latter achieving state-of-the-art results Narayan et al, 2020).…”

Section: Vq 2 Amentioning

confidence: 99%

All You May Need for VQA are Image Captions

Changpinyo¹,

Kukliansky²,

Szpektor³

et al. 2022

Preprint

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Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation. In this paper, we propose a method that automatically derives VQA examples at volume, by leveraging the abundance of existing image-caption annotations combined with neural models for textual question generation. We show that the resulting data is of high-quality. VQA models trained on our data improve state-of-theart zero-shot accuracy by double digits and achieve a level of robustness that lacks in the same model trained on human-annotated VQA data.

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“…QG has progressed rapidly due to new datasets and model improvements. Many different QG models have been proposed, starting for simple vanilla Sequence to Sequence Neural Networks models (seq2seq) (Du et al, 2017;Zhou et al, 2017;Yuan et al, 2017) to the more recent transformer-based models (Dong et al, 2019;Chan and Fan, 2019;Varanasi et al, 2020;Narayan et al, 2020;Bao et al, 2020). Some QG systems use manual linguistic features in their models (Harrison and Walker, 2018;Khullar et al, 2018;Liu et al, 2019a;Dhole and Manning, 2020), some consider how to select question-worthy content (Du and Cardie, 2017;Scialom et al, 2019;, and some systems explicitly model question types (Duan et al, 2017;Sun et al, 2018;Kang et al, 2019;Zhou et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

Ghanem¹,

Lutz²,

Rivard³

et al. 2022

Preprint

View full text Add to dashboard Cite

Reading is integral to everyday life, and yet learning to read is a struggle for many young learners. During lessons, teachers can use comprehension questions to increase engagement, test reading skills, and improve retention. Historically such questions were written by skilled teachers, but recently language models have been used to generate comprehension questions. However, many existing Question Generation (QG) systems focus on generating literal questions from the text, and have no way to control the type of the generated question. In this paper, we study QG for reading comprehension where inferential questions are critical and extractive techniques cannot be used. We propose a two-step model (HTA-WTA) that takes advantage of previous datasets, and can generate questions for a specific targeted comprehension skill. We propose a new reading comprehension dataset that contains questions annotated with story-based reading comprehension skills (SBRCS), allowing for a more complete reader assessment. Across several experiments, our results show that HTA-WTA outperforms multiple strong baselines on this new dataset. We show that the HTA-WTA model tests for strong SCRS by asking deep inferential questions.

show abstract

QURIOUS: Question Generation Pretraining for Text Generation

Cited by 4 publications

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Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

All You May Need for VQA are Image Captions

Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

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