Self-Guided Contrastive Learning for BERT Sentence Representations (2106.07345v1)

Abstract: Although BERT and its variants have reshaped the NLP landscape, it still remains unclear how best to derive sentence embeddings from such pre-trained Transformers. In this work, we propose a contrastive learning method that utilizes self-guidance for improving the quality of BERT sentence representations. Our method fine-tunes BERT in a self-supervised fashion, does not rely on data augmentation, and enables the usual [CLS] token embeddings to function as sentence vectors. Moreover, we redesign the contrastive learning objective (NT-Xent) and apply it to sentence representation learning. We demonstrate with extensive experiments that our approach is more effective than competitive baselines on diverse sentence-related tasks. We also show it is efficient at inference and robust to domain shifts.

Self-Guided Contrastive Learning for BERT Sentence Representations

The paper "Self-Guided Contrastive Learning for BERT Sentence Representations" focuses on addressing the challenge of deriving effective sentence embeddings from pre-trained LLMs like BERT. While models such as BERT have achieved significant advancements in NLP, their application for sentence-level representation often necessitates further tuning to capture semantic relationships efficiently. This paper proposes a novel approach using self-guided contrastive learning aimed at improving the quality of sentence embeddings derived from BERT without relying on additional data augmentation strategies.

Methodology Overview

The core contribution of the paper is a self-guided contrastive learning framework which utilizes the inherent structure of BERT's architecture to produce superior sentence embeddings. The method involves:

1. Self-Guided Contrastive Learning: The paper introduces a contrastive learning technique that utilizes intermediate layer representations within BERT as training signals, hence termed "self-guided." This technique circumvents the need for data augmentation by exploiting these internal representations as virtual positive samples.
2. Customized NT-Xent Loss: The authors adapt the NT-Xent loss, a popular contrastive loss in computer vision, to better suit sentence representation learning. The modifications focus on optimizing the loss to prioritize alignment between the final sentence embedding and its intermediate positive samples from BERT.
3. Efficient Architecture: The approach involves cloning BERT into two instances—one fixed and one trainable. This setup preserves the BERT base's expressive power while nudging the trainable instance towards better holistic sentence representations.

Experimental Evaluation

The proposed method was benchmarked against several baselines, including mean pooling and other contrastive techniques using data augmentation methods. Key findings include:

• The self-guided method (SG and SG-OPT) consistently outperformed baselines on a diverse set of semantic textual similarity (STS) tasks, demonstrating robust performance improvements in producing meaningful sentence vectors across datasets.
• The method showed particular strength in multilingual settings when tested with a cross-lingual zero-shot transfer task, demonstrating the potential of the approach beyond monolingual datasets.
• Inference efficiency is highlighted as a significant advantage of the method due to its reliance on the [CLS] token embedding, which simplifies the downstream application without necessitating additional computational overhead for pooling operations.

Implications and Future Directions

The implications of this work lie primarily in improving the deployment of BERT-like models for sentence-level tasks with enhanced efficiency and accuracy. The self-guided learning paradigm proposed by the authors shows promise in further enhancing linguistic representation without extensive computational costs or complicated preprocessing steps.

This paper sets the groundwork for future research directions in unsupervised fine-tuning of pre-trained transformers, exploring the interplay between architecture-based intrinsic representations and task-specific adjustments. Furthermore, this scalable approach can be extended to newer models and applied in various NLP applications, facilitating more effective and efficient semantic learning.

Overall, by refining how sentence-level data is utilized within BERT architectures, this paper demonstrates significant improvements in generating sentence embeddings that are both semantic-rich and computationally efficient, broadening their applicability in practical NLP tasks.