Multilingual Chatbot Parallel Intent Translation for Faster Responses
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Solution Overview
Problem
Existing chatbot systems struggle to accurately and efficiently translate inputs from users speaking languages other than the default language, leading to prolonged response times.
Innovation Solution
A multilingual chatbot system utilizing a dual-track approach with a translational track for literal translation and an intelligence track leveraging AI and ML to process inputs in parallel, reducing response times by internally translating and analyzing user intents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single translation track is used to translate requests in non-default languages, then translation accuracy can be maintained, but response time increases significantly
Solution Approach 1:
The translation process is segmented into two independent tracks: a translational track for accurate translation and an intelligence track for rapid processing. Each track handles different aspects of translation independently, allowing the system to maintain accuracy while reducing overall response time by processing both tracks in parallel rather than sequentially
Solution Approach 2:
The intelligence track performs a simplified form of translation that may not be fully accurate but is sufficient for understanding basic user intent. This partial translation action occurs in parallel with the complete translation in the translational track, allowing the system to respond quickly to simple queries while maintaining accuracy for complex translations
2Adaptability or versatility
If a third-party translation application is used, then comprehensive language support is achieved, but response time increases by one or more orders of magnitude
Solution Approach 1:
The system introduces an intermediary intelligence track that processes translations internally using pre-trained models. This intermediary layer handles common translation scenarios quickly without needing to externalize to third-party applications, reserving third-party translation services only for complex cases that require comprehensive language support
Solution Approach 2:
Translation models are pre-trained and cached in advance for commonly encountered languages and phrases. When a translation request arrives, the system first checks if a pre-computed translation exists in the cache, returning it immediately if found, thereby avoiding the need to call external translation services for routine queries
Data Source
AI summary
A method for reducing response time of an utterance received in a multilingual chatbot system may be provided. The utterance may be received in a language other than a default language with respect to the multilingual chatbot system. The method may include receiving a request utterance from a remote user device and determining that the request utterance is not in the default language. In response to the determining, the method may include transmitting the request utterance, in parallel, to both a translational track for translating the request utterance and an intelligence track for translating the request utterance. The method may include computing a first intent of the translated request utterance at the translational track. The method may include computing a second intent based on a ML request utterance. The method may include determining that the first intent matches the second intent and following the determining, generating a response utterance.