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- W2970705401 abstract "This paper explores the task of answer-aware questions generation. Based on the attention-based pointer generator model, we propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. Our joint-learning model enables the encoder to learn a better representation of the input sequence, which will guide the decoder to generate more coherent and fluent questions. On both SQuAD and MARCO datasets, our multi-task learning model boosts the performance, achieving state-of-the-art results. Moreover, human evaluation further proves the high quality of our generated questions." @default.
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- W2970705401 date "2019-01-01" @default.
- W2970705401 modified "2023-09-26" @default.
- W2970705401 title "Multi-Task Learning with Language Modeling for Question Generation" @default.
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- W2970705401 doi "https://doi.org/10.18653/v1/d19-1337" @default.
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