Neural machine translation decoding acceleration method based on discrete attention mechanism

A machine translation and attention technology, applied in the field of neural machine translation decoding acceleration, can solve problems such as the inability to take advantage of low-precision numerical calculations, and achieve the effects of improving real-time response speed, reducing hardware costs, and reducing computational complexity

Active Publication Date: 2020-05-19
沈阳雅译网络技术有限公司
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Problems solved by technology

[0009] In view of the fact that the machine translation method in the prior art relies too much on single-precision and double-precision floating point and cannot take advantage of low-precision numerical calculations, the technical problem to be solved by the present invention is to provide a neural machine translatio

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  • Neural machine translation decoding acceleration method based on discrete attention mechanism
  • Neural machine translation decoding acceleration method based on discrete attention mechanism
  • Neural machine translation decoding acceleration method based on discrete attention mechanism

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Embodiment Construction

[0039] The present invention will be further elaborated below in conjunction with the accompanying drawings of the description.

[0040]The present invention will optimize the decoding speed of the neural machine translation system based on the attention mechanism from the perspective of low-precision numerical operations, aiming to greatly increase the decoding speed of the translation system at the cost of a small performance loss, and achieve a balance between performance and speed .

[0041] A kind of neural machine translation decoding acceleration method based on discrete attention mechanism of the present invention comprises the following steps:

[0042] 1) Construct a training parallel corpus and a neural machine translation model based on the attention mechanism, use the parallel corpus to generate a machine translation vocabulary, and further train to obtain model parameters after training convergence, as a baseline system;

[0043] 2) Convert some parameters of the...

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Abstract

The invention discloses a neural machine translation decoding acceleration method based on a discrete attention mechanism, and the method comprises the steps: constructing a neural machine translationmodel for training a parallel corpus and based on an attention mechanism; generating a machine translation word list through the parallel corpus; carrying out the further training to obtain a model parameter after training convergence, and enabling the model parameter to serve as a baseline system; converting partial parameters of the attention mechanism in the network into integer values by scaling the model parameter values, and mapping floating-point numbers into the integer section of the integer values; replacing the normalized part which cannot be subjected to integer calculation with alinear structure which is beneficial to integer calculation; controlling whether all numerical values participate in operation or not through a threshold value before the linear structure; and adjusting the selected threshold value on 1%-2% of data randomly extracted from the training data, so that a better translation result can be achieved. According to the method, the real-time corresponding speed can be improved on the basis of the latest implementation of rapid reasoning and on the premise that the model performance is hardly reduced.

Description

technical field [0001] The invention relates to a neural machine translation decoding acceleration technology, in particular to a neural machine translation decoding acceleration method based on a discrete attention mechanism. Background technique [0002] Machine translation (Machine Translation) is the use of computer programs to translate a natural language into another natural language, which belongs to the category of computational linguistics. In 1949, Warren Weaver published a memorandum titled "Translation", which marked that machine translation based on modern computers officially entered the stage of history. Machine translation not only involves human cognition of its own language and way of thinking, but also involves many fields such as artificial intelligence, information theory, knowledge engineering, and software engineering. It is a discipline that intersects multiple technologies in depth. In the past ten years, the research and industrialization of machin...

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Application Information

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IPC IPC(8): G06F40/42G06F40/58G06N3/04G06N3/08
CPCG06N3/04G06N3/08Y02D10/00
Inventor 杜权朱靖波肖桐张春良
Owner 沈阳雅译网络技术有限公司
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