Contrastive Training Data Generation for Multilingual Embedding Models
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Solution Overview
Problem
Conventional methods for fine-tuning embedding models for chatbots face challenges in obtaining sufficient training data, especially for multilingual and diverse conversational and non-conversational utterances, leading to resource-intensive and slow training processes.
Innovation Solution
A method involving pre-trained embedding models is used with unsupervised contrastive learning to generate positive and negative utterances from anchor utterances through translation, perturbation, and augmentation operations, applying a contrastive loss function to fine-tune the model for entity-aware embedding of words and sentences in multiple languages and domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to fine-tune embedding models, then training data can be obtained, but the training process becomes resource-intensive and slow
Solution Approach 1:
The patent applies preliminary action by using a pre-trained embedding model as a starting point rather than training from scratch. The pre-trained model provides initial weights and architectural knowledge that can be directly utilized, eliminating the need to build the embedding model from zero and significantly reducing the computational resources and time required for fine-tuning.
Solution Approach 2:
The patent employs parameter changes by adjusting the learning rate and other training parameters during the fine-tuning process. By carefully controlling these parameters, the model can efficiently adapt to domain-specific data without requiring excessive computational resources, thereby improving training speed while maintaining energy efficiency.
2Reliability
If sufficient training data is obtained for multilingual and diverse utterances, then model performance improves, but the training process becomes more complex and time-consuming
Solution Approach 1:
The patent applies universality by training a single embedding model to handle multiple languages and diverse utterance types (conversational and non-conversational) simultaneously. The pre-trained model's universal architecture can accommodate various languages and domains through fine-tuning, eliminating the need to train separate models for each language or domain, thus reducing overall training time while maintaining high performance.
Solution Approach 2:
The patent uses parameter changes by adjusting the fine-tuning process to accommodate multilingual and diverse data. By modifying learning rates and training parameters dynamically, the model can efficiently learn patterns across multiple languages and domains without requiring proportional increases in training time, thereby maintaining reliability while reducing time loss.
3Device complexity
If a single model is used to maintain embedding quality across different domains and languages, then device complexity is reduced, but the model requires more sophisticated fine-tuning techniques
Solution Approach 1:
The patent applies universality by designing a single multi-functional embedding model that can serve multiple domains and languages. This universal model reduces device complexity by consolidating what would otherwise require multiple separate models into one architecture, while the fine-tuning process adapts this single model to various specific use cases through controlled parameter adjustments.
Solution Approach 2:
The patent uses preliminary action by pre-training the embedding model on diverse, high-quality data before domain-specific fine-tuning. This preliminary training establishes a robust foundation that simplifies subsequent fine-tuning processes, as the model already possesses general linguistic and semantic knowledge that can be adapted to specific domains without requiring complex retraining procedures.
Data Source
AI summary
Techniques are provided for generating training data to facilitate fine-tuning embedding models. Training data including anchor utterances is obtained. Positive utterances and negative utterances are generated from the anchor utterances. Tuples including the anchor utterances, the positive utterances, and the negative utterances are formed. Embeddings for the tuples are generated and a pre-trained embedding model is fine-tuned based on the embeddings. The fine-tuned model can be deployed to a system.