- 专利标题: Natural language generation through character-based recurrent neural networks with finite-state prior knowledge
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申请号: US15408526申请日: 2017-01-18
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公开(公告)号: US10049106B2公开(公告)日: 2018-08-14
- 发明人: Raghav Goyal , Marc Dymetman
- 申请人: Xerox Corporation
- 申请人地址: US CT Norwalk
- 专利权人: Xerox Corporation
- 当前专利权人: Xerox Corporation
- 当前专利权人地址: US CT Norwalk
- 代理机构: Fay Sharpe LLP
- 主分类号: G06F17/27
- IPC分类号: G06F17/27 ; G06F17/28 ; G10L25/30
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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