Contrastive Label Propagation for Sparse Intent Annotation
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Solution Overview
Problem
Artificial intelligence implementations face challenges due to the scarcity and variable quality of labeled data, which are complex and time-consuming to obtain, hindering practical applications.
Innovation Solution
Propagating labels through a sparsely labeled dataset using contrastive learning projections, transforming coarse representations into fine-grained representations by maximizing distances between dissimilar points and minimizing distances between similar points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If labeled data are obtained through manual annotation, then label accuracy is improved, but time consumption and complexity increase
Solution Approach 1:
The system enables unlabeled data to annotate themselves by leveraging the pre-trained model's predictions. The model generates initial labels for unlabeled data, which are then refined through contrastive learning projections that compare embeddings of labeled and unlabeled data points, allowing the system to self-annotate without human intervention.
Solution Approach 2:
The patent introduces contrastive learning projections as an intermediary mechanism between labeled and unlabeled data. This intermediary process projects embeddings into a shared space where similarities and dissimilarities are computed, enabling indirect label transfer from labeled to unlabeled data points without direct human annotation.
2Reliability
If more labeled data are collected, then model performance is improved, but data acquisition complexity increases
Solution Approach 1:
The system performs preliminary action by using a pre-trained model to generate initial embeddings and predictions for all data points before label propagation. This preliminary embedding phase prepares the data structure in advance, enabling efficient subsequent label propagation without requiring additional manual labeling efforts.
Solution Approach 2:
The patent applies copying by transferring label information from labeled data points to their corresponding unlabeled counterparts through the contrastive learning projection framework. The system copies embeddings and label associations from the labeled portion to the unlabeled portion, effectively replicating label knowledge without re-annotation.
3Speed
If coarse representations are used from pre-trained models, then processing speed is improved, but label propagation accuracy deteriorates
Solution Approach 1:
The system changes parameters by transforming embeddings from the pre-trained model's original space into a new contrastive learning projection space. This parameter transformation involves computing similarity and dissimilarity metrics that reweight the embedding dimensions, optimizing them specifically for label propagation tasks while retaining the computational efficiency of the original embeddings.
Solution Approach 2:
The patent introduces another dimension by adding a projection layer that maps embeddings from the original pre-trained model space into an enhanced contrastive learning space. This dimensional transformation allows the system to preserve the speed benefits of pre-trained embeddings while adding new computational dimensions for improved label propagation accuracy through contrastive comparisons.
Data Source
AI summary
Some embodiments can include methods and related systems to project coarse representations of natural language interactions into fine-grained representations using contrastive learning projections. Some embodiments can maximize a first set of distances between dissimilar points and anchor points in the fine-grained representations, and minimize a second set of distances between similar points and the anchor points in the fine-grained representations. Some embodiments can then propagate labels from labeled projections to unlabeled projections based on a similarity metric.