Multilingual Chat Agent Workflows Without Real-Time Translation
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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 non-synchronized corpuses of text for different languages.
Innovation Solution
A method involving translating the corpus of training data and responses into target languages, training a natural language processing model, and deploying a translated workflow to enable chat agents to support multiple languages without additional latency or translation errors.
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 different languages, but computational resource consumption increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-translating the training corpus into multiple target languages before model training. This allows the NLP model to be trained directly on translated data, eliminating the need for real-time translation during inference. The translation work is performed in advance during the model preparation phase, not during actual chat operations.
Solution Approach 2:
The patent uses copying by creating translated copies of the training corpus in multiple languages. Instead of translating user inputs in real-time, the system creates parallel training datasets in different languages, allowing the model to learn and respond directly in those languages without requiring runtime translation operations.
2Adaptability or versatility
If real-time translation is used for multi-language chat agents, then users can communicate in their native languages, but additional latency is introduced into the communication process
Solution Approach 1:
The patent eliminates communication latency by performing translation in advance during model training rather than in real-time during user interactions. The NLP model is pre-trained on translated training data, so when users communicate in target languages, the model responds directly without requiring real-time translation, thus avoiding additional latency.
3Adaptability or versatility
If real-time translation services are used to support multiple languages, then chat agents can understand and respond in different languages, but translation errors may occur leading to misidentification of user intent
Solution Approach 1:
The patent creates translated copies of the training corpus in multiple target languages and trains the NLP model directly on these translated datasets. This allows the model to learn the semantic patterns and intent structures of each language natively, rather than relying on translation services during inference. The model develops language-specific understanding that improves reliability in intent identification.
4Manufacturing precision
If separate corpuses of text are maintained for different languages, then each language can have optimized training data, but the complexity of maintaining and synchronizing multiple corpuses increases
Solution Approach 1:
The patent applies universality by using a single source training corpus that serves multiple functions: it is translated into multiple target languages to create training datasets for different languages. This single source corpus approach allows the same base content to be reused across multiple language models, reducing the complexity of maintaining separate corpuses while still providing language-optimized training data.
5Manufacturing precision
If separate corpuses of text are maintained for different languages, then language-specific training can be achieved, but synchronization and updates across languages become difficult
Solution Approach 1:
The patent uses a universal source corpus that serves as the foundation for all language-specific training datasets. When updates or corrections are needed, changes are made to the single source corpus, and the same updated content can be translated and propagated to all target language datasets, ensuring automatic synchronization across all languages without manual coordination.
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.