Language-Agnostic Neural Network for Multilingual Title Standardization
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Solution Overview
Problem
Existing language-dependent title standardization models in social networks face challenges in handling titles in multiple languages, particularly due to the lack of sufficient training data and the need for language identification and normalization, which limits their reliability across different languages.
Innovation Solution
A language-agnostic machine learning model is developed to standardize titles by training a neural network that can predict standardizations without language identification or normalization, utilizing loan words and relations between languages, and requiring only taxonomical candidates, thus allowing for incomplete taxonomies and simplifying the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate models are used for each language, then language-specific accuracy may be improved, but data insufficiency in individual languages leads to unreliable results
Solution Approach 1:
The patent combines multiple language-specific models into a single unified language-agnostic model. Instead of training separate models for each language, the invention uses one model that processes titles in any language by representing them as sequences of characters or subwords, thereby pooling data across all languages to improve reliability while maintaining standardization accuracy.
Solution Approach 2:
The patent creates a universal model that can handle multiple languages without requiring language-specific training data or separate model instances. The model achieves multi-functionality by using language-independent tokenization and processing mechanisms, allowing a single model to standardize titles across diverse languages reliably.
2Measurement precision
If language identification and normalization steps are added, then language-specific processing accuracy may be improved, but system complexity and processing time increase
Solution Approach 1:
The patent extracts and removes the language identification and normalization steps from the title standardization pipeline. By directly feeding raw multilingual titles into the language-agnostic model without preliminary language detection or normalization, the system simplifies the architecture while maintaining effective processing of compound nouns and non-separated words in various languages.
Solution Approach 2:
The model achieves universal processing capability across languages without requiring language-specific preprocessing. The single model handles diverse linguistic structures (including compound nouns and non-separated words) through its language-agnostic design, eliminating the need for separate language identification and normalization components.
3Manufacturing precision
If complete taxonomies are required for all languages, then standardization completeness may be improved, but data collection and model training become significantly more difficult
Solution Approach 1:
The patent accepts that taxonomies may be incomplete for certain languages and still achieves effective standardization. The model can standardize titles even when the taxonomy does not contain all possible language-specific terms, by leveraging cross-lingual patterns and loan words. This partial action approach allows the system to function effectively without requiring exhaustive multilingual taxonomy coverage.
Solution Approach 2:
The patent uses English or other dominant language taxonomies as intermediaries for languages with incomplete taxonomies. The model can map titles from languages with sparse taxonomy coverage to standardized terms by leveraging relationships with better-documented languages, effectively using intermediate language representations to bridge gaps in taxonomy completeness.
Data Source
AI summary
In an example embodiment, a system is provided whereby a machine learning model is trained to predict a standardization for a given raw title. A neural network may be trained whose input is a raw title (such as a query string) and a list of candidate titles (either title identifications in a taxonomy, or English strings), which produces a probability that the raw title and each candidate belong to the same title. The model is able to standardize titles in any language included in the training data without first having to perform language identification or normalization of the title. Additionally, the model is able to benefit from the existence of “loan words” (words adopted from a foreign language with little or no modification) and relations between languages.