Prefix-Constrained Neural Machine Translation Decoding
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Solution Overview
Problem
Current automated translation technologies, particularly neural machine translation systems, face challenges in achieving high accuracy and speed in translating phrases from a source language to a target language, as they often rely on statistical methods that require extensive computational resources and lack efficient interactive processes.
Innovation Solution
A neural machine translation system is implemented with a network interface circuit and memory that stores translation data and instructions, utilizing a terminology dictionary and performing constrained prefix decoding with beam search to predict target words, allowing for interactive and efficient translation by focusing on user-provided prefixes and leveraging attention mechanisms for terminology alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical techniques are used for translation, then translation can be performed, but translation accuracy and speed are insufficient
Solution Approach 1:
The patent replaces traditional statistical machine translation mechanisms with a neural machine translation system that uses neural networks to process and translate text. This substitution enables the system to achieve both high translation accuracy through learned representations and improved speed through efficient neural network inference, resolving the contradiction between accuracy and speed that plagues statistical approaches.
2Measurement precision
If neural machine translation is used, then translation accuracy improves, but computational resource requirements increase
Solution Approach 1:
The patent segments the translation process into distinct phases: prefix-constrained decoding that processes user-provided prefixes, beam search for generating candidate translations, and terminology dictionary lookup for validation. This segmentation allows the system to focus computational resources on critical path operations while using efficient approximations elsewhere, reducing overall computational resource consumption while maintaining high translation accuracy.
3Measurement precision
If beam search is used for decoding, then translation quality improves, but decoding time increases
Solution Approach 1:
The patent performs preliminary action by incorporating a terminology dictionary that pre-stores valid translation pairs and phrases. During beam search decoding, the system consults this pre-prepared dictionary to quickly validate candidate translations and prune invalid paths, eliminating the need to fully explore the entire search space. This preliminary preparation significantly reduces decoding time while maintaining high translation quality through accurate terminology matching.
Data Source
AI summary
An apparatus has a network interface circuit to receive a source sentence from a network connected client device. A processor is connected to the network interface circuit. A memory is connected to the processor. The memory stores translation data and instructions executed by the processor. The instructions executed by the processor operate a neural machine translation system. A translation hypothesis is formed from a prefix of a target sentence comprising an initial sequence of target words supplied by a user through an interface. The hypothesis is generated by the neural machine translation system that performs a constrained prefix decoding that repeatedly predicts a next word from previous target words. A suffix of the target sentence comprising a final sequence of words corresponding to a final sequence of words in the source sentence is formed using a beam search that constrains translation to match the prefix.