Contrastive Self-Supervised Learning for Commonsense Reasoning
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Solution Overview
Problem
Current machine learning models, particularly in natural language processing, lack the ability to perform effective commonsense reasoning due to insufficient training corpora and reliance on superficial cues, leading to difficulties in resolving ambiguities such as pronoun references.
Innovation Solution
A self-supervised pre-training task using pairwise sentences with minimal supervision, where the difference between sentences is only one 'trigger' word, and a contrastive loss function is employed to enforce mutual exclusivity among candidates, allowing the model to learn deeper word relationships and improve generalization for commonsense reasoning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If current machine learning models use standard training corpora and supervision methods, then training data availability is improved, but commonsense reasoning capability deteriorates due to reliance on superficial cues
Solution Approach 1:
The system performs self-supervised learning by automatically generating training signals from the data itself without external annotations. The contrastive loss function enables the model to learn from unlabelled sentence pairs by identifying mutual exclusivity relationships, allowing the system to serve its own training needs without human supervision.
Solution Approach 2:
The invention changes the learning paradigm from supervised to self-supervised, and introduces contrastive loss as a new parameter for measuring training effectiveness. By using mutual exclusivity as the training signal instead of traditional supervised labels, the model learns deeper commonsense relationships while maintaining training data availability.
2Productivity
If minimal supervision is used for self-supervised pre-training, then training efficiency is improved, but learning depth deteriorates without adequate enforcement of commonsense relationships
Solution Approach 1:
The contrastive loss function provides continuous feedback during training by measuring the mutual exclusivity between candidate answers. This feedback mechanism guides the model to adjust its representations to better capture commonsense relationships, ensuring adequate learning depth even with minimal external supervision.
Solution Approach 2:
The system performs self-supervised pre-training as a preliminary step before fine-tuning on specific tasks. This preliminary action allows the model to learn general commonsense relationships from unlabelled data, preparing it for more specialized tasks while maintaining training efficiency.
3Measurement precision
If contrastive loss function with mutual exclusivity enforcement is used, then commonsense reasoning accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies contrastive loss selectively to enforce mutual exclusivity only where needed for commonsense reasoning, rather than applying it universally to all training data. This partial action approach maintains computational tractability while achieving improved reasoning accuracy on critical tasks.
4Manufacturing precision
If supervised fine-tuning is applied after self-supervised pre-training, then task-specific performance is improved, but overfitting to superficial cues worsens
Solution Approach 1:
Self-supervised pre-training serves as a preliminary action that equips the model with robust commonsense reasoning capabilities before task-specific fine-tuning. This preliminary learning of fundamental relationships protects against overfitting to superficial cues during subsequent supervised training on specific tasks.
Data Source
AI summary
In an example embodiment, a self-supervised learning task is used for training commonsense-aware representations in a minimally supervised fashion and a pair level mutual-exclusive loss is used to enforce commonsense knowledge during representation learning. This helps to exploit the mutual-exclusive nature of the training samples of commonsense reasoning corpora. Given two pieces of input where the only difference between them are trigger pieces of data, it may be postulated that the pairwise pronoun disambiguation is mutually exclusive. This idea is formulated using a contrastive loss and then this is used to update the language model.