Question-type Driven Question Generation

Zhou, Wenjie; Zhang, Minghua; Wu, Yunfang

doi:10.18653/v1/d19-1622

Cited by 41 publications

(36 citation statements)

References 11 publications

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“…• NQG-Knowledge [16], DLPH [12]: auxiliary-informationenhanced question generation models with extra inputs such as knowledge or difficulty. • Self-training-EE [38], BERT-QG-QAP [51], NQG-LM [55], CGC-QG [27] and QType-Predict [56]: multi-task question generation models with auxiliary tasks such as question answering, language modeling, question type prediction and so on.…”

Section: Evaluating Acs-aware Question Generationmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Evaluating Acs-aware Question Generationmentioning

confidence: 99%

“…[27] jointly predicts the words in input that is related to the aspect of the targeting output question and will be copied to the question. [56] predicts the question type based on the input answer and context. [55] incorporates language modeling task to help question generation.…”

Section: Related Workmentioning

confidence: 99%

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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“…Du et al (2017) and Zhou et al (2017) followed the paradigm of sequenceto-sequence and showed promising results when combining rich features and attention mechanism. and Zhou et al (2019) incorporated answer-focused information to improve the relevance between answers and questions. Liu et al (2019) and Chen et al (2020) introduced graph networks to estimate significant contents in the source context.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we categorized question types according to the interrogative words commonly used in general questions. Specifically, they are classified into 8 types -what, who, how, when, which, where, why and other (Zhou et al, 2019). We combine the contextual information s and the selector representation z as the input.…”

Section: Question Type Predictormentioning

confidence: 99%

Diversify Question Generation with Continuous Content Selectors and Question Type Modeling

Wang

Rao

Zhang³

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Generating questions based on answers and relevant contexts is a challenging task. Recent work mainly pays attention to the quality of a single generated question. However, question generation is actually a one-to-many problem, as it is possible to raise questions with different focuses on contexts and various means of expression. In this paper, we explore the diversity of question generation and come up with methods from these two aspects. Specifically, we relate contextual focuses with content selectors, which are modeled by a continuous latent variable with the technique of conditional variational auto-encoder (CVAE). In the realization of CVAE, a multimodal prior distribution is adopted to allow for more diverse content selectors. To take into account various means of expression, question types are explicitly modeled and a diversity-promoting algorithm is proposed further. Experimental results on public datasets show that our proposed method can significantly improve the diversity of generated questions, especially from the perspective of using different question types. Overall, our proposed method achieves a better trade-off between generation quality and diversity compared with existing approaches.

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“…Recent neural network-based methods have achieved promising results on QG, most of which are based on the seq2seq attention framework (Du et al, 2017;Gao et al, 2018;Kim et al, 2018;Zhou et al, 2019b), enriched with lexical features Sun et al, 2018;Song et al, 2018) or enhanced by copy mechanism (Du and Cardie, 2018;Sun et al, 2018;Zhou et al, 2019a).…”

Section: Sentencementioning

confidence: 99%

How to Ask Good Questions? Try to Leverage Paraphrases

Jia

Zhou

Sun

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Given a sentence and its relevant answer, how to ask good questions is a challenging task, which has many real applications. Inspired by human's paraphrasing capability to ask questions of the same meaning but with diverse expressions, we propose to incorporate paraphrase knowledge into question generation(QG) to generate human-like questions. Specifically, we present a two-hand hybrid model leveraging a self-built paraphrase resource, which is automatically conducted by a simple back-translation method. On the one hand, we conduct multi-task learning with sentence-level paraphrase generation (PG) as an auxiliary task to supplement paraphrase knowledge to the task-share encoder. On the other hand, we adopt a new loss function for diversity training to introduce more question patterns to QG. Extensive experimental results show that our proposed model obtains obvious performance gain over several strong baselines, and further human evaluation validates that our model can ask questions of high quality by leveraging paraphrase knowledge.

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Question-type Driven Question Generation

Cited by 41 publications

References 11 publications

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

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

Diversify Question Generation with Continuous Content Selectors and Question Type Modeling

How to Ask Good Questions? Try to Leverage Paraphrases

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