Neural Machine Translation for Implicit Payload Identification
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Solution Overview
Problem
Conventional digital assistants face challenges in accurately identifying translation payloads from user requests due to variations in input formats, leading to errors in translation processes that require multiple steps including speech recognition and natural language understanding.
Innovation Solution
A machine-learning translation system is trained using adapted payloads formulated as translation requests, allowing for direct processing of user requests without explicit payload identification, enabling accurate translation regardless of input format and enabling continuous improvement with additional user requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple steps including speech recognition and natural language understanding are performed to identify translation payload, then the translation request can be processed, but the error accumulates and translation accuracy deteriorates
Solution Approach 1:
The patent combines speech recognition, natural language understanding, and translation processing into a single integrated neural machine translation system. The system processes the entire user request end-to-end without separate steps for payload identification, speech-to-text conversion, and translation, thereby reducing error accumulation while maintaining comprehensive processing capability.
Solution Approach 2:
The patent introduces a neural machine translation system as an intermediary that directly processes user requests from speech input to target language output. This intermediary system eliminates the need for explicit payload identification by traditional NLU components and sequential processing steps, reducing error propagation while maintaining translation quality.
2Adaptability or versatility
If explicit payload identification is performed to handle variations of user requests, then translation can be executed, but the process becomes more complex and error-prone
Solution Approach 1:
The patent changes the operational parameters of the translation system by using neural network models that can directly process varied user request formats without explicit payload identification. The system adjusts from rule-based payload extraction to neural-based end-to-end processing, maintaining adaptability to different user input styles while simplifying the overall process.
Solution Approach 2:
The neural machine translation system performs self-service by automatically adapting to various user request formats without requiring explicit payload identification. The system inherently handles variations in user input through its training data and model architecture, eliminating the need for complex payload extraction logic.
3Reliability
If traditional multi-step translation process is used, then translation functionality is provided, but power consumption increases due to multiple processing steps
Solution Approach 1:
The patent merges multiple power-consuming processing steps (speech recognition, NLU, payload identification, translation) into a single neural machine translation process. This consolidation reduces the cumulative power consumption of sequential operations while maintaining complete translation functionality through the integrated system.
Data Source
AI summary
Systems and processes for operating an electronic device to train a machine-learning translation system are described. In one process, a first set of training data is obtained. The first set of training data includes at least one payload in a first language and a translation of the at least one payload in a second language. The process further includes obtaining one or more templates for adapting the at least one payload; adapting the at least one payload using the one or more templates to generate at least one adapted payload formulated as a translation request; generating a second set of training data based on the at least one adapted payload; and training the machine-learning translation system using the second set of training data.