Multilingual Embeddings for Cross-Lingual NLP
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Natural language processing tools are not readily available in languages with fewer speakers due to the high cost and time required for collecting training data in every language for every task, making it challenging to develop cross-lingual NLP tools effectively.
Innovation Solution
A system that trains a multilingual embedding on unlabeled multilingual documents, using word embeddings as features to generalize NLP models across languages, allowing for the application of NLP models trained in one language to another, even without in-language training data, by employing artificial code-switching and constraint-based methods to create a unified vocabulary and shared representation across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If training data is collected in every language for every task, then NLP tool availability is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent segments the training data requirement by language, using only in-language training data for the target language while obtaining source language training data through machine translation. This segmentation reduces the data collection burden for low-resource languages while maintaining NLP tool availability across multiple languages.
Solution Approach 2:
The patent introduces machine translation as an intermediary mechanism to bridge high-resource and low-resource languages. By translating training data from high-resource source languages to low-resource target languages, the system enables NLP tool development without direct data collection in every language, significantly reducing time and resource requirements.
2Adaptability or versatility
If training data is collected in every language for every task, then NLP tool availability is improved, but development cost increases
Solution Approach 1:
The patent makes training data universal by using machine translation to adapt training data from source languages to target languages. A single training corpus in a high-resource language can serve multiple low-resource languages through translation, eliminating the need for separate data collection efforts for each language and reducing overall development resources required.
Solution Approach 2:
The patent creates copies of training data through machine translation, generating target language training data by copying and translating source language data. This copying approach allows reuse of expensive-to-collect training data across multiple languages without requiring original data collection in each language, significantly reducing development costs.
3Productivity
If machine translation is used to obtain training data, then data collection effort is reduced, but translation quality and accuracy may be compromised
Solution Approach 1:
The patent incorporates feedback mechanisms where the quality of machine-translated training data is evaluated and used to refine the translation process. By monitoring translation quality and adjusting translation parameters or selecting better source languages, the system maintains training data quality while benefiting from the efficiency of machine translation for data preparation.
Data Source
AI summary
A natural language processing (“NLP”) manager is provided that manages NLP model training. An unlabeled corpus of multilingual documents is provided that span a plurality of target languages. A multilingual embedding is trained on the corpus of multilingual documents as input training data, the multilingual embedding being generalized across the target languages by modifying the input training data and/or transforming multilingual dictionaries into constraints in an underlying optimization problem. An NLP model is trained on training data for a first language of the target languages, using word embeddings of the trained multilingual embedding as features. The trained NLP model is applied for data from a second of the target languages, the first and second languages being different.