Generating Questions Based on Semi-Automated and End-to-End Neural Network

Xia, Tianci; Sun, Haopeng; Zhao, Xibin; Song, Wei; Guo, Yumiao

doi:10.32604/cmc.2019.05860

Cited by 4 publications

(1 citation statement)

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“…While these works were not related to automatic question generation tasks, they did provide the correct forms of Tibetan questions. In an attempt at Tibetan sentence-level question generation, Xia et al [25] proposed a semi-automated method to generate questions; however, their work required human participation. To achieve automatically generated questions, Sun et al [26] combined generative adversarial networks (GANs) [27] with reinforcement learning to automatically generate Tibetan questions.…”

Section: Related Workmentioning

confidence: 99%

Tibetan Question Generation Based on Sequence to Sequence Model

Sun¹,

Chen²,

Chen³

et al. 2021

Computers, Materials &Amp; Continua

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As the dual task of question answering, question generation (QG) is a significant and challenging task that aims to generate valid and fluent questions from a given paragraph. The QG task is of great significance to question answering systems, conversational systems, and machine reading comprehension systems. Recent sequence to sequence neural models have achieved outstanding performance in English and Chinese QG tasks. However, the task of Tibetan QG is rarely mentioned. The key factor impeding its development is the lack of a public Tibetan QG dataset. Faced with this challenge, the present paper first collects 425 articles from the Tibetan Wikipedia website and constructs 7,234 question-answer pairs through crowdsourcing. Next, we propose a Tibetan QG model based on the sequence to sequence framework to generate Tibetan questions from given paragraphs. Secondly, in order to generate answer-aware questions, we introduce an attention mechanism that can capture the key semantic information related to the answer. Meanwhile, we adopt a copy mechanism to copy some words in the paragraph to avoid generating unknown or rare words in the question. Finally, experiments show that our model achieves higher performance than baseline models. We also further explore the attention and copy mechanisms, and prove their effectiveness through experiments.

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Section: Related Workmentioning

confidence: 99%