Autonomous Chat Agent Translation Workflow for Low-Latency Multilingual NLP
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Solution Overview
Problem
Existing methods for deploying autonomous chat agents in multiple languages face challenges such as high computational resource consumption, latency, and translation errors, as well as the need for maintaining separate and independent corpuses of text for each language.
Innovation Solution
A method involving the translation of training data and responses into a second language, followed by training a natural language processing machine-learning model to identify user intents and provide responses in that language, without requiring real-time translation or separate corpuses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If real-time translation is used to enable multi-language support, then chat agents can communicate with users in multiple languages, but computational resource consumption increases and latency is introduced
Solution Approach 1:
The patent translates training data and responses into multiple languages before training the machine learning model, rather than performing translation in real-time during user interactions. This preliminary translation action enables the model to directly process and generate responses in multiple languages without requiring computational resources for real-time translation, thus resolving the contradiction between multi-language support and computational resource consumption
2Adaptability or versatility
If real-time translation is used for multi-language communication, then users can interact in their native language, but additional latency is introduced into the communication flow
Solution Approach 1:
The translation of training data and responses is performed in advance during the model training phase, not during real-time user interactions. This eliminates the need for real-time translation operations that would introduce latency, allowing the chat agent to respond immediately in the user's language without time loss
3Measurement precision
If separate independent corpuses of text are maintained for each language, then language-specific accuracy can be optimized, but device complexity and maintenance burden increase
Solution Approach 1:
The patent merges multiple language corpuses into a single unified training corpus by translating training data and responses into multiple languages and combining them. This unified corpus approach maintains language-specific accuracy while reducing the complexity of managing separate independent corpuses, as the model learns from all languages simultaneously through a single integrated training process
Data Source
AI summary
Disclosed are various embodiments for automated translations for autonomous chat agents. A build service can send a translation request to a machine translation service, the translation request comprising training data in a first language and the translation request specifying a second language. The build service can then receive translated training data from the machine translation service, the translated training data having been translated from the training data into the second language. Next, the build service can create a translated workflow that comprises a translated machine learning model and a translated intent. Subsequently, the build service can add the translated training data to the translated workflow and train the translated machine learning model using the translated training data.