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Systems and methods for neural clinical paraphrase generation

机译:神经临床翻译的系统和方法

摘要

The present disclosure pertains to a paraphrase generation system. The system comprises one or more hardware processors and/or other components. The system is configured to obtain a training corpus. The training corpus comprises language and known paraphrases of the language. The system is configured to generate, based on the training corpus, a word-level attention-based model and a character-level attention-based model. The system is configured to provide one or more candidate paraphrases of a natural language input based on both the word-level and character-level attention-based models. The word-level attention-based model is a word-level bidirectional long short term memory (LSTM) network and the character-level attention-based model is a character-level bidirectional LSTM network. The word-level and character level LSTM networks are generated based on words and characters in the training corpus. In some embodiments, the LSTM networks are stacked residual LSTM networks comprising residual connections between stacked layers of a given LSTM network.
机译:本公开涉及一种解释生成系统。该系统包括一个或多个硬件处理器和/或其他组件。该系统被配置为获取培训语料库。培训语料库包括语言和已知语言的释义。该系统被配置为基于培训语料库生成基于词级关注的模型和基于角色级的模型。该系统被配置为基于单词级和基于字符级的模型提供自然语言输入的一个或多个候选释义。基于单词级别的主题模型是一个字级双向短期内存(LSTM)网络,字符级关注的模型是一个字符级双向LSTM网络。基于培训语料库中的单词和字符生成单词级和字符级LSTM网络。在一些实施例中,LSTM网络是堆叠的残差LSTM网络,包括在给定的LSTM网络的堆叠层之间的残差连接。

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