DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations (2006.03659v4)

Abstract: Sentence embeddings are an important component of many NLP systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labelled data, limiting their usefulness to languages and domains where labelled data is abundant. In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data. When used to extend the pretraining of transformer-based LLMs, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. Importantly, our experiments suggest that the quality of the learned embeddings scale with both the number of trainable parameters and the amount of unlabelled training data. Our code and pretrained models are publicly available and can be easily adapted to new domains or used to embed unseen text.

An Expert Overview of DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations

The paper presents DeCLUTR, a self-supervised method aimed at learning universal sentence embeddings without the need for labeled data. This approach addresses a significant limitation in NLP where the highest performing sentence embeddings often rely on abundant labeled data, thus restricting their applicability across diverse languages and domains.

Methodology

DeCLUTR draws inspiration from deep metric learning, applying a contrastive loss to sentence encoding. This self-supervised objective bridges the gap between unsupervised and supervised pretraining of sentence encoders by leveraging large amounts of unlabeled data.

• Contrastive Loss: DeCLUTR uses a contrastive method to learn representations by maximizing agreement between segments sampled from the same document. This involves training an encoder to minimize distances between embedding pairs (anchor-positive) and maximize distances between mismatched pairs (anchor-negative).
• Transformer Extension: The method extends pretraining from transformer-based LLMs, such as RoBERTa, by incorporating an additional self-supervised contrastive loss alongside the masked LLM (MLM) objective. This combination improves embedding quality and enhances scalability with data volume and model size.

Evaluation and Results

The research evaluates DeCLUTR using the SentEval benchmark, encompassing 28 tasks designed to test various linguistic properties and transferability of sentence representations. A comparison with existing supervised and unsupervised methods is conducted.

• Performance Metrics: DeCLUTR achieves competitive results with state-of-the-art supervised techniques on downstream tasks without using any labeled data. The model demonstrates improvement over its baseline transformer models, particularly in challenging areas with limited labeled datasets.
• Probing Tasks: In probing tasks, which assess the capture of linguistic phenomena, DeCLUTR maintains performance analogous to its pretraining models, contrasting with other methods that often experience significant degradation.

Implications and Future Directions

The findings suggest that well-designed self-supervised objectives can replace or supplement labeled datasets for universal sentence embedding tasks. Such strategies are not only cost-effective but also adaptable for extensive multilingual settings. Future research may explore further scalability of these self-supervised methods, possibly enhancing model capabilities by integrating even larger-scale unlabelled corpora.

This work underscores a pivotal shift in NLP towards reducing dependency on labeled data by harnessing the agility and depth of self-supervised learning techniques, paving the way for more inclusive and versatile AI applications.

The authors have made their code publicly accessible, facilitating reutilization and adaptation for specialized domains, which could significantly impact domain-specific LLMing and multilingual NLP advancements.