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NATURAL LANGUAGE GENERATION THROUGH CHARACTER-BASED RECURRENT NEURAL NETWORKS WITH FINITE-STATE PRIOR KNOWLEDGE
NATURAL LANGUAGE GENERATION THROUGH CHARACTER-BASED RECURRENT NEURAL NETWORKS WITH FINITE-STATE PRIOR KNOWLEDGE
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机译:通过基于字符的递归神经网络的有限状态优先知识自然语言生成
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摘要
A method and a system for generating a target character sequence from a semantic representation including a sequence of characters are provided. The method includes adapting a target background model, built from a vocabulary of words, to form an adapted background model. The adapted background model accepts subsequences of an input semantic representation as well as words from the vocabulary. The input semantic representation is represented as a sequence of character embeddings, which are input to an encoder. The encoder encodes each of the character embeddings to generate a respective character representation. A decoder then generates a target sequence of characters, based on the set of character representations. At a plurality of time steps, a next character in the target sequence is selected as a function of a previously generated character(s) of the target sequence and the adapted background model.
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