NLG Template GUI for Grammatical Localization
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Solution Overview
Problem
Existing natural language generation (NLG) technologies face challenges in efficiently and accurately generating grammatical and natural language responses in target languages, requiring significant resources and expertise, often resulting in flawed templates that can prolong or terminate interactions between humans and automated systems.
Innovation Solution
A graphical user interface (GUI) is developed to facilitate the efficient generation of target language NLG templates by allowing users, including non-linguists, to interact with curated source language templates and primitives, enabling the selection and scoring of target language primitives based on their relevance and historical usage, which helps in generating accurate and grammatically correct output examples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative machine learning models are used to generate natural language output, then the system can provide dynamic and flexible responses, but the output may lack grammaticality and naturalness, leading to user confusion and prolonged interactions
Solution Approach 1:
The system segments natural language generation into two distinct components: a generative model that creates diverse response content and a separate template-based system that ensures grammatical correctness. This segmentation allows each component to specialize - the generative model provides flexibility while the template system guarantees grammaticality and naturalness.
2Adaptability or versatility
If generative models are trained for multiple languages, then language coverage is improved, but the resource intensity and time required for data accumulation and labeling increase significantly
Solution Approach 1:
Instead of training separate generative models for each language, the system copies and adapts proven NLG templates from source languages to target languages. This copying approach leverages existing linguistically validated templates, avoiding the time-consuming process of accumulating and labeling training data for each new language while maintaining high-quality grammatical output.
3Manufacturing precision
If NLG templates are manually crafted for target languages by computational linguists, then template accuracy is improved, but the resource intensity and time required for template creation increase significantly
Solution Approach 1:
The system performs preliminary action by pre-crafting and validating NLG templates in source languages where linguistic expertise is already invested. These pre-validated templates are then systematically adapted to target languages through automated processes, eliminating the need for computational linguists to manually craft templates for each target language while preserving accuracy.
Solution Approach 2:
The system introduces an intermediary translation and adaptation layer between source language templates and target language output. This intermediary process handles the complex linguistic transformations automatically, allowing templates to be created once in source languages and efficiently adapted to multiple target languages without requiring expert manual intervention for each language.
4Productivity
If machine translation is used to translate system responses from source language to target language, then localization speed is improved, but the target language output may become non-grammatical and unnatural
Solution Approach 1:
The system introduces NLG templates as an intermediary structure between machine translation and final output. Instead of directly translating raw generated text, the system translates into template structures that inherently enforce grammatical rules and natural language patterns of the target language, ensuring both speed and grammaticality.
Solution Approach 2:
The system changes the parameter of grammar enforcement from being a post-processing constraint to being an inherent structural property of the output. By organizing translation output to fit predefined grammatical templates, the system automatically ensures grammaticality and naturalness without requiring additional validation steps.
Data Source
AI summary
Implementations relate to effectively localizing system responses, that include dynamic information, to target language(s), such that the system responses are grammatical and/or natural in the target language(s). Some of those implementations relate to various techniques for resource efficient generation of templates for a target language. Some versions of those implementations relate to resource efficient generation of target language natural language generation (NLG) templates and, more particularly, to techniques that enable a human user to generate a target language NLG template more efficiently and/or with greater accuracy. The more efficient target language NLG template generation enables less utilization of various client device resources and/or can mitigate the risk of flawed NLG templates being provided for live use in one or more systems.