Core Concepts
CoT-BERT proposes a two-stage approach for sentence representation, leveraging Chain-of-Thought and contrastive learning to enhance unsupervised models like BERT.
Abstract
Unsupervised sentence representation learning aims to transform input sentences into fixed-length vectors enriched with semantic information.
Recent progress in the field has bridged the gap between unsupervised and supervised strategies.
CoT-BERT introduces a two-stage approach for sentence representation: comprehension and summarization.
The method outperforms baselines without external components, showcasing the effectiveness of the proposed techniques.
CoT-BERT's extended InfoNCE Loss and template denoising strategy contribute to its success.
Stats
Unsupervised sentence representation learning aims to transform input sentences into fixed-length vectors enriched with intricate semantic information.
Recent progress within this field, propelled by contrastive learning and prompt engineering, has significantly bridged the gap between unsupervised and supervised strategies.
CoT-BERT proposes a two-stage approach for sentence representation: comprehension and summarization.
The method outperforms several robust baselines without necessitating additional parameters.
CoT-BERT introduces a superior contrastive learning loss function that extends beyond the conventional InfoNCE Loss.
Quotes
"CoT-BERT transcends a suite of robust baselines without necessitating other text representation models or external databases."
"Our extensive experimental evaluations indicate that CoT-BERT outperforms several robust baselines without necessitating additional parameters."