Task-Specific Language Clusters for Multilingual Learning

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Solution Overview

Problem

Current multilingual learning approaches cluster languages based on language families, which can lead to negative interference due to semantic changes and grammatical differences, affecting tasks like text classification and sentiment analysis, as languages within the same family may have different interpretations and grammatical structures.

Innovation Solution

A data-driven method for generating task-specific language clusters by identifying and weighting positive and negative interferences between languages, using models to train on specific tasks and aggregating weights to form clusters that minimize interference, improving downstream tasks such as text classification and sentiment analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If languages are clustered based on language families for joint learning, then training cost is reduced, but negative interference occurs due to semantic changes and grammatical differences

Engineering Contradiction:

Improvetraining costVSAvoidlearning accuracy

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The patent changes the clustering parameter from language family classification to task-specific interference metrics. By computing interference weights based on semantic changes and grammatical differences specific to each task, the system dynamically determines which languages should be clustered together for joint learning, thereby reducing negative interference while maintaining training efficiency

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent applies different clustering strategies to different language pairs based on their specific interference characteristics. Rather than applying a universal clustering rule to all languages, the system evaluates pairwise interference metrics and creates task-specific clusters that account for local semantic and grammatical differences between language pairs

Inventive Principle:

Principle #3Local quality

2Productivity

If languages within the same family are clustered together, then joint learning efficiency is improved, but confusing signals increase due to different interpretations and grammatical structures

Engineering Contradiction:

Improvejoint learning efficiencyVSAvoidconfusing signals

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent employs a feedback mechanism where the system computes interference weights by comparing task performance metrics when languages are clustered versus when they are trained separately. This feedback loop allows the system to identify which language clusters produce confusing signals and adjust the clustering configuration accordingly, balancing joint learning efficiency with information preservation

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20230410682A1Task-specific language sets for multilingual learning

Publication Date: 2023.12.21 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A method, a structure, and a computer system for multilingual learning. The exemplary embodiments may include training, for each language in a set of two or more languages, a model for a task and identifying one or more important words appearing in at least two of the models. The exemplary embodiments may further include weighting one or more conflicts and one or more overlaps between the one or more important words, as well as generating a cluster of at least two languages of the set based on an aggregate of the weighting.