Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation (2022.coling-1)
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| Challenge: | Existing question generation models treat input passage as a sequence-to-sequence generative task, but they are not aware of text structure. |
| Approach: | They propose to model text structure as answer position and syntactic dependency and propose a mask attention mechanism to make syntaktic structure of input passage accessible. |
| Outcome: | The proposed model outperforms the strong pre-trained model ProphetNet on a SQuAD dataset and achieves competitive results with the state-of-the-art model. |
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Zenan Xu, Daya Guo, Duyu Tang, Qinliang Su, Linjun Shou, Ming Gong, Wanjun Zhong, Xiaojun Quan, Daxin Jiang, Nan Duan
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| Challenge: | Current neural network-based questions generation techniques take only one or two sentences as input. |
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| Challenge: | Existing work for natural question generation ignores the input passage or hard-codes answer positions. |
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| Challenge: | Question Generation (QG) is the production of meaningful questions given a set of input passages and corresponding answers. |
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| Challenge: | Pretraining techniques have achieved great success on table-to-text generation. |
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| Challenge: | Existing work is limited in using small benchmarks with high test-train overlaps. |
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| Challenge: | Pretrained language models perform structural understanding tasks that focus on understanding one aspect of the text. |
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| Challenge: | Extractive Machine Reading Comprehension (MRC) is a challenging field in the field of Natural Language Processing. |
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