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CONTEXTUAL TEXT GENERATION FOR QUESTION ANSWERING AND TEXT SUMMARIZATION WITH SUPERVISED REPRESENTATION DISENTANGLEMENT AND MUTUAL INFORMATION MINIMIZATION
CONTEXTUAL TEXT GENERATION FOR QUESTION ANSWERING AND TEXT SUMMARIZATION WITH SUPERVISED REPRESENTATION DISENTANGLEMENT AND MUTUAL INFORMATION MINIMIZATION
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机译:关于问题的文本生成,有关监督表示解剖和互信最小化的文本摘要
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摘要
Methods and systems for disentangled data generation include accessing a dataset including pairs, each formed from a given input text structure and a given style label for the input text structures. An encoder is trained to disentangle a sequential text input into disentangled representations, including a content embedding and a style embedding, based on a subset of the dataset, using an objective function that includes a regularization term that minimizes mutual information between the content embedding and the style embedding. A generator is trained to generate a text output that includes content from the style embedding, expressed in a style other than that represented by the style embedding of the text input.
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