Multi-lingual Query Interpretation via Language Invariant Signals
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Solution Overview
Problem
Current natural language interfaces to databases (NLIDB) systems are not equipped to accurately process natural language queries in languages different from their native language, leading to incomplete or inaccurate interpretations.
Innovation Solution
A system that generates language invariant signals to interpret query intent independently of domain and language specific training, using components like annotation, interpretation, and translation to facilitate multi-lingual query interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NLIDB systems use native language processing only, then interpretation accuracy is improved, but multi-lingual capability deteriorates
Solution Approach 1:
The patent introduces language invariant signals as an intermediary representation that mediates between the input natural language query in any language and the backend query execution. These signals serve as a universal intermediate form that captures the semantic intent without being tied to any specific language, allowing accurate interpretation while supporting multiple languages.
Solution Approach 2:
The system transforms the query from language-specific parameters (words, syntax) to language-invariant parameters (semantic signals, intent representations). By changing the parameter space from surface-level linguistic features to deep semantic features, the system achieves both accuracy and multi-lingual support.
2Reliability
If NLIDB systems perform language-specific training, then domain accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent creates a universal processing framework that handles multiple languages and domains through a single system architecture. The language invariant signals enable one system to perform the function of multiple language-specific systems, reducing overall complexity while maintaining domain accuracy through the universal semantic representation.
3Ease of operation
If NLIDB systems require domain specific training, then query understanding is improved, but ease of deployment deteriorates
Solution Approach 1:
The language invariant signals act as a mediator that decouples query understanding from domain-specific knowledge. By representing queries in a universal intermediate form, the system achieves good query understanding without requiring separate training for each domain, thereby improving ease of deployment.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate multi-lingual query interpretation. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an annotation component that generates one or more language invariant signals, an interpretation component that generates a complete query intent using the one or more language invariant signals, and a translation component that processes the complete query intent to an executable backend query to facilitate multi-lingual query interpretation. In one or more embodiments, the translation component can be operatively connected with the interpretation component to generate a zero-shot transfer of the one or more language invariant signals.