Harvesting Paragraph-level Question-Answer Pairs from Wikipedia

Du, Xinya; Cardie, Claire

doi:10.18653/v1/p18-1177

Cited by 148 publications

(139 citation statements)

References 23 publications

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“…Previous work on question generation has made extensive use of MT techniques. Du et al (2017) use a Seq2Seq based model to generate questions conditioned on context-answer pairs, and build on this work by preprocessing the context to resolve coreferences and adding a pointer network (Du and Cardie, 2018). Similarly, Zhou et al (2018) use a part-of-speech tagger to augment the embedding vectors.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Rewards for Question Generation Models

Hosking

Riedel

2019

Proceedings of the 2019 Conference of the North

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Recent approaches to question generation have used modifications to a Seq2Seq architecture inspired by advances in machine translation. Models are trained using teacher forcing to optimise only the one-step-ahead prediction. However, at test time, the model is asked to generate a whole sequence, causing errors to propagate through the generation process (exposure bias). A number of authors have suggested that optimising for rewards less tightly coupled to the training data might counter this mismatch. We therefore optimise directly for various objectives beyond simply replicating the ground truth questions, including a novel approach using an adversarial discriminator that seeks to generate questions that are indistinguishable from real examples. We confirm that training with policy gradient methods leads to increases in the metrics used as rewards. We perform a human evaluation, and show that although these metrics have previously been assumed to be good proxies for question quality, they are poorly aligned with human judgement and the model simply learns to exploit the weaknesses of the reward source.

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Section: Introductionmentioning

confidence: 99%

Evaluating Rewards for Question Generation Models

Hosking

Riedel

2019

Proceedings of the 2019 Conference of the North

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“…While WS-TB is related to the approaches mentioned before, DQG is is also related to question generation (QG). Most of the previous work in QG is in the context of reading comprehension (e.g., Du et al, 2017;Subramanian et al, 2018;Zhao et al, 2018;Du and Cardie, 2018) or QG for question answering (Duan et al, 2017). They substantially differ from our approach because they generate questions based on specific answer spans, while DQG generates a new title from a question's body that can be used as a question duplicate.…”

Section: Duplicates Answers Bodiesmentioning

confidence: 96%

Neural Duplicate Question Detection without Labeled Training Data

Rücklé

Moosavi

Gurevych

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Supervised training of neural models to duplicate question detection in community Question Answering (cQA) requires large amounts of labeled question pairs, which are costly to obtain. To minimize this cost, recent works thus often used alternative methods, e.g., adversarial domain adaptation. In this work, we propose two novel methods: (1) the automatic generation of duplicate questions, and (2) weak supervision using the title and body of a question. We show that both can achieve improved performances even though they do not require any labeled data. We provide comprehensive comparisons of popular training strategies, which provides important insights on how to 'best' train models in different scenarios. We show that our proposed approaches are more effective in many cases because they can utilize larger amounts of unlabeled data from cQA forums. Finally, we also show that our proposed approach for weak supervision with question title and body information is also an effective method to train cQA answer selection models without direct answer supervision.

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“…The problem is usually formulated as answeraware question generation, where the position of answer is provided as input. Most of them take advantage of the encoder-decoder framework with attention mechanism [10,11,22,27,39,41,53]. Different approaches incorporate the answer information into generation model by different strategies, such as answer position indicator [27,53], separated answer encoding [23], embedding the relative distance between the context words and the answer [42] and so on.…”

Section: Related Workmentioning

confidence: 99%

“…Automatically generating question-answer pairs from unlabeled text passages is of great value to many applications, such as assisting the training of machine reading comprehension systems [10,44,45], generating queries/questions from documents to improve search engines [17], training chatbots to get and keep a conversation going [40], generating exercises for educational purposes [7,18,19], and generating FAQs for web documents [25]. Many question-answering tasks such as machine reading comprehension and chatbots require a large amount of labeled samples for supervised training, acquiring which is time-consuming and costly.…”

Section: Introductionmentioning

confidence: 99%

“…Most existing neural question generation approaches try to solve the answer-aware question generation problem, where an answer chunk and the surrounding passage are provided as an input to the model while the output is the question to be generated. They formulate the task as a Sequence-to-Sequence (Seq2Seq) problem, and design various encoder, decoder, and input features to improve the quality of generated questions [10,11,22,27,39,41,53]. However, answeraware question generation models are far from sufficient, since question generation from a passage is inherently a one-to-many mapping.…”

Section: Introductionmentioning

confidence: 99%

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Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

Liu

Wei

Niu

et al. 2020

Proceedings of the Web Conference 2020

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The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, improves question-answering and machine reading comprehension tasks, and helps a chatbot to keep the conversation flowing with a human. Existing question generation models are ineffective at generating a large amount of high-quality question-answer pairs from unstructured text, since given an answer and an input passage, question generation is inherently a one-to-many mapping. In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions. Our system consists of: i) an information extractor, which samples from the text multiple types of assistive information to guide question generation; ii) neural question generators, which generate diverse and controllable questions, leveraging the extracted assistive information; and iii) a neural quality controller, which removes low-quality generated data based on text entailment. We compare our question generation models with existing approaches and resort to voluntary human evaluation to assess the quality of the generated question-answer pairs. The evaluation results suggest that our system dramatically outperforms state-of-the-art neural question generation models in terms of the generation quality, while being scalable in the meantime. With models trained on a relatively smaller amount of data, we can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.

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Harvesting Paragraph-level Question-Answer Pairs from Wikipedia

Cited by 148 publications

References 23 publications

Evaluating Rewards for Question Generation Models

Evaluating Rewards for Question Generation Models

Neural Duplicate Question Detection without Labeled Training Data

Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

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