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• W2890166583 abstract "Question generation, the task of automatically creating questions that can be answered by a certain span of text within a given passage, is important for question-answering and conversational systems in digital assistants such as Alexa, Cortana, Google Assistant and Siri. Recent sequence to sequence neural models have outperformed previous rule-based systems. Existing models mainly focused on using one or two sentences as the input. Long text has posed challenges for sequence to sequence neural models in question generation – worse performances were reported if using the whole paragraph (with multiple sentences) as the input. In reality, however, it often requires the whole paragraph as context in order to generate high quality questions. In this paper, we propose a maxout pointer mechanism with gated self-attention encoder to address the challenges of processing long text inputs for question generation. With sentence-level inputs, our model outperforms previous approaches with either sentence-level or paragraph-level inputs. Furthermore, our model can effectively utilize paragraphs as inputs, pushing the state-of-the-art result from 13.9 to 16.3 (BLEU_4)." @default.
• W2890166583 created "2018-09-27" @default.
• W2890166583 creator A5041070092 @default.
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• W2890166583 date "2018-01-01" @default.
• W2890166583 modified "2023-10-18" @default.
• W2890166583 title "Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks" @default.
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• W2890166583 doi "https://doi.org/10.18653/v1/d18-1424" @default.
• W2890166583 hasPublicationYear "2018" @default.
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