ParaQG: A System for Generating Questions and Answers from Paragraphs

Kumar, Vikas; Muneeswaran, Sivaanandh; Ramakrishnan, Ganesh; Li, Yuan Fang

doi:10.18653/v1/d19-3030

Cited by 10 publications

(10 citation statements)

References 19 publications

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“…For QG specifically, building on early research with attention and the basic encoder-decoder setup (Zhou et al 2018), recent works have started exploring transformers (Chan and Fan 2019), variational encoders (Lee et al 2020), reinforcement learning (Wang et al 2020b), semantic information (Pan et al 2020) and future n-gram prediction (Qi et al 2020). However, research addressing content selection and answer unaware QG is still preliminary with some previous works employing supervision for training an answer span selection module alongside QG (Du and Cardie Subramanian et al 2018) or by simply treating noun phrases and named entities as potential answer cues for QG (Lewis, Denoyer, and Riedel 2019;Kumar et al 2019).…”

Section: Related Workmentioning

confidence: 99%

PaintTeR: Automatic Extraction of Text Spans for Generating Art-Centered Questions

Gollapalli

Tham

et al. 2022

AAAI

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We propose PaintTeR, our Paintings TextRank algorithm for extracting art-related text spans from passages on paintings. PaintTeR combines a lexicon of painting words curated automatically through distant supervision with random walks on a large-scale word co-occurrence graph for ranking passage spans for artistic characteristics. The spans extracted with PaintTeR are used in state-of-the-art Question Generation and Reading Comprehension models for designing an interactive aid that enables gallery and museum visitors focus on the artistic elements of paintings. We provide experiments on two datasets of expert-written passages on paintings to showcase the effectiveness of PaintTeR. Evaluations by both gallery experts as well as crowdworkers indicate that our proposed algorithm can be used to select relevant and interesting art-centered questions. To the best of our knowledge, ours is the first work to effectively fine-tune question generation models using minimal supervision for a low-resource, specialized context such as gallery visits.

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Section: Related Workmentioning

confidence: 99%

PaintTeR: Automatic Extraction of Text Spans for Generating Art-Centered Questions

Gollapalli

Tham

et al. 2022

AAAI

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“…Also, systems like [46] introduce a pair-to-sequence model that captures the interaction between the question asked and the given paragraph. Specific systems like ParaQG [20] try to generate the questions from the paragraph. Systems like [35] pick up the keywords from the question and paragraph and match them using RNN.…”

Section: Related Workmentioning

confidence: 99%

Automatic question-answer pairs generation and question similarity mechanism in question answering system

2021

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With the swift growth of the information over the past few years, taking full benefit is increasingly essential. Question Answering System is one of the promising methods to access this much information. The Question Answering System lacks humans’ common sense and reasoning power and cannot identify unanswerable questions and irrelevant questions. These questions are answered by making unreliable and incorrect guesses. In this paper, we address this limitation by proposing a Question Similarity mechanism. Before a question is posed to a Question-Answering system, it is compared with possible generated questions of the given paragraph, and then a Question Similarity Score is generated. The Question Similarity mechanism effectively identifies the unanswerable and irrelevant questions. The proposed Question Similarity mechanism incorporates a human way of reasoning to identify unanswerable and irrelevant questions. This mechanism can avoid the unanswerable and irrelevant questions altogether from being posed to the Question Answering system. It helps the Question Answering Systems to focus only on the answerable questions to improve their performance. Along with this, we introduce an application of the Question Answering System that generates the question-answer pairs given a passage and is useful in several fields.

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“…Most of these state-of-the-art models for question generation use a single sentence as input to generate questions which is not useful for generating questions of educational content available as pre-recorded videos. ParaQG [25], an advanced NLP model is introduced to tackle such problems and capable of generating questions from paragraphs. ParaQG is based on a combination of NLP techniques such as Seq2Seq model with dynamic dictionaries, the copy mechanism and the global sparse-max attention.…”

Section: Ai-based Automatic Question Generation (Aqg)mentioning

confidence: 99%

“…VidVersityQG generates short-answer questions from the two input kinds: (1) the target segment chosen by a teacher in the second phase, and (2) the keywords chosen in the third phase. We employ an advanced NLP model that uses a deep learning neural network, named ParaQG [25], to automatically generate short-answer questions. ParaQG can generate fluent, meaningful and relevant questions from a paragraph with a given target answers.…”

Section: Short-answer Question Generationmentioning

confidence: 99%

An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers

Kang

Forkan

Jayaraman

et al. 2021

Preprint

Self Cite

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There has been a recent and rapid shift to digital learning hastened by the pandemic but also influenced by ubiquitous availability of digital tools and platforms now, making digital learning ever more accessible. An integral and one of the most difficult part of scaling digital learning and teaching is to be able to assess learner's knowledge and competency. An educator can record a lecture or create digital content that can be delivered to thousands of learners but assessing learners is extremely time consuming. In the paper, we propose an Artificial Intelligence (AI)-based solution namely Vid-VersityQG for generating questions automatically from pre-recorded video lectures. The solution can automatically generate different types of assessment questions (including short answer, multiple choice, true/false and fill in the blank questions) based on contextual and semantic information inferred from the videos. The proposed solution takes a human-centred approach, wherein teachers are provided the ability to modify/edit any AI generated questions. This approach encourages trust and engagement of teachers in the use and implementation of AI in education. The AI-based solution was evaluated for its accuracy in generating questions by 7 experienced teaching

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ParaQG: A System for Generating Questions and Answers from Paragraphs

Cited by 10 publications

References 19 publications

PaintTeR: Automatic Extraction of Text Spans for Generating Art-Centered Questions

PaintTeR: Automatic Extraction of Text Spans for Generating Art-Centered Questions

Automatic question-answer pairs generation and question similarity mechanism in question answering system

An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers

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