CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering

Yue, Xiang; Zhang, Xinliang Frederick; Yao, Zhiliang; Lin, Simon; Sun, Huan

doi:10.48550/arxiv.2010.16021

“…QG has been used to solve many real-life problems. For example, QG in conversational dialogue (Gu et al, 2021;Shen et al, 2021;Liu et al, 2021b) where models were taught to ask a series of coherent questions grounded in a QA style, QG based on visual input (Mostafazadeh et al, 2016;Shin et al, 2018;Shukla et al, 2019), and QG for deep questions such as mathematical, curiosity-driven, clinical, and examinationtype questions (Liyanage and Ranathunga, 2019;Yue et al, 2020;Jia et al, 2021).…”

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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“…QG has been used to solve many real-life problems. For example, QG in conversational dialogue (Gu et al, 2021;Shen et al, 2021;Liu et al, 2021b) where models were taught to ask a series of coherent questions grounded in a QA style, QG based on visual input (Mostafazadeh et al, 2016;Shin et al, 2018;Shukla et al, 2019), and QG for deep questions such as mathematical, curiosity-driven, clinical, and examinationtype questions (Liyanage and Ranathunga, 2019;Yue et al, 2020;Jia et al, 2021).…”

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

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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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“…Question Generation (QG) is the task of generating questions from a given context and optionally, some answers. The research area on QG has developed exponentially with the task getting more popular in areas such as education, commercial applications (e.g chatbots, dialogue systems) and healthcare [1].…”

Section: Introductionmentioning

confidence: 99%

Automated question generation and question answering from Turkish texts

Akyon¹,

Cavusoglu²,

Cengiz³

et al. 2021

Preprint

1

0

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While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience and resources. To reduce the expenses associated with the manual construction of questions and to satisfy the need for a continuous supply of new questions, automatic question generation (QG) techniques can be utilized. However, compared to automatic question answering (QA), QG is a more challenging task. In this work, we fine-tune a multilingual T5 (mT5) transformer in a multitask setting for QA, QG and answer extraction tasks using a Turkish QA dataset. To the best of our knowledge, this is the first academic work that attempts to perform automated text-to-text question generation from Turkish texts. Evaluation results show that the proposed multi-task setting achieves state-of-the-art Turkish question answering and question generation performance over TQuADv1, TQuADv2 datasets and XQuAD Turkish split. The source code and pre-trained models are available at this https URL.

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CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering

Cited by 3 publications

References 40 publications

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

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

Automated question generation and question answering from Turkish texts

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