Multi-Task Learning with Language Modeling for Question Generation

Zhou, Wenjie; Zhang, Minghua; Wu, Yunfang

doi:10.18653/v1/d19-1337

Cited by 28 publications

(26 citation statements)

References 22 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%

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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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Section: Evaluating Acs-aware Question Generationmentioning

confidence: 99%

“…[56] predicts the question type based on the input answer and context. [55] incorporates language modeling task to help question generation. [51] utilizes question paraphrasing and question answering tasks to regularize the QG model to generate semantically valid questions.…”

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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“…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%

“…For current mainstream neural network-based methods on QG, most approaches utilize the Seq2Seq model with attention mechanism (Du et al, 2017;Zhao et al, 2018b;Zhou et al, 2019a). To obtain better representations of the input sequence and answer, the answer position and token lexical features are treated as supplements for the neural encoder Song et al, 2018;Kim et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

“…Similar to other text generation tasks, many works on QG also employ copy or pointer mechanism to overcome the OOV problem (Du and Cardie, 2018;Sun et al, 2018;Zhang and Bansal, 2019). Recently, Zhou et al (2019a) employ language modeling (LM) as an auxiliary task to enrich the encoder representations. In this paper, we adopt this work as one of the baseline models, since their universal model is easy to implement and achieves promising results for QG.…”

Section: Related Workmentioning

confidence: 99%

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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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