Universal Hypothesis Ranking Model for Multilingual Dialog
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Multi-domain dialogue systems face challenges in accurately ranking dialog hypotheses across different languages and locales due to language-dependent recognition, leading to errors in domain classification and system responses.
Innovation Solution
A universal hypothesis ranking model is developed, applicable to multiple languages and locales, which analyzes language-independent features to rank dialog hypotheses, reducing the need for language-specific models and facilitating expansion to new languages with minimal retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If language-dependent recognition models are used for hypothesis ranking, then recognition accuracy for specific languages is improved, but the system cannot be easily expanded to new languages and requires separate models for each language/ locale
Solution Approach 1:
The patent creates a universal hypothesis ranking model that processes multiple languages and locales through a single unified architecture. The model uses language-independent features such as semantic frames, domain intents, slot values, and dialog context that are common across all languages, allowing one model to serve multiple language functions without requiring separate language-specific models
Solution Approach 2:
The patent extracts and removes language-specific elements from the hypothesis ranking process by focusing on language-independent features. By taking out the language-dependent components and retaining only the universal semantic and contextual features, the system achieves both accuracy and language agnosticism
2Measurement precision
If separate language-specific hypothesis ranking models are maintained, then accuracy for each language is optimized, but maintenance overhead and system complexity increase
Solution Approach 1:
The patent merges multiple language-specific hypothesis ranking models into a single universal model. By combining the functionality of separate models into one unified system that processes all languages through common language-independent features, the patent reduces model management complexity while maintaining ranking capabilities across diverse languages
3Adaptability or versatility
If language-independent features are used for universal model processing, then ease of expansion to new languages is improved, but potential loss of language-specific nuances may occur
Solution Approach 1:
The patent introduces semantic frames as an intermediary representation layer between the input dialog and the hypothesis ranking process. These semantic frames capture the essential meaning and intent of user input in a language-independent manner, serving as a mediator that preserves linguistic nuances while enabling universal processing across different languages
Data Source
AI summary
Non-limiting examples of the present disclosure describe generation and application of a universal hypothesis ranking model to rank/re-re-rank dialog hypotheses. An input is received through a user interface of an application for dialog processing. A plurality of dialog hypotheses are generated based on input understanding processing of the received input. The plurality of dialog hypotheses are ranked using a universal hypothesis ranking model that is applicable to a plurality of languages and locales. The ranking of the plurality of dialog hypotheses comprises using the universal hypothesis ranking model to analyze language independent features of the plurality of dialog hypotheses for policy determination. Other examples are also described including examples directed to generation of the universal hypothesis ranking model.