Self-supervised learning (SSL), often dubbed representation learning, orbits the concept of leveraging unlabeled datasets to extract constructive patterns and connections. This learning paradigm occupies an increasing significance within Natural Language Processing (NLP), the branch of artificial intelligence that handles human language understanding and generation.

• Data Efficiency: Self-supervised learning makes effective use of massive, unlabeled datasets that would otherwise require immense time and resources for manual labeling, making it a data-efficient model.
• Automatic Feature Learning: SSL negates the necessity for manual feature extraction or engineering, a labor-intensive and error-prone process. Instead, it automates the detection of high-level, abstract features from raw data.
• Generality: With SSL, one can apply the learned representations to a broad range of downstream tasks, thereby demonstrating its wide applicability and versatility.
• High Performance: Innovative self-supervised models like GPT-3 have exhibited state-of-the-art performance, even rivaling that of human beings in diverse NLP tasks.
• Superiority over Unsupervised Learning: While unsupervised learning merely seeks to identify clusters or groups in data, SSL goes a step further by predicting certain elements of the data, thus yielding superior semantic understanding and fine-grained representations.

The rise of SSL in NLP has led to breakthroughs in various fields like text summarization, translation, sentiment analysis, chatbots, and more.

Successful implementation of SSL in NLP necessitates careful planning and precise execution. Key steps in this process include defining the learning problem correctly, selecting appropriate objectives and auxiliary tasks, devising accurate self-supervision signals, and regularizing the model effectively. Additionally, continuous monitoring of the model's learning process is paramount for obtaining high-quality language representations. Thus, SSL serves as a valuable tool in the NLP toolkit, one that exploits the vast reservoir of available unlabeled text data.

• Leverages unlabeled data: Labeled data for NLP is often scarce and costly to obtain. SSL exploits the abundance of unlabeled data on the Internet to learn powerful language representations, making it a practical, cost-effective solution.
• Eliminates manual feature engineering: In traditional NLP, manual feature engineering was requisite – an often labor-intensive and error-prone process. SSL, however, automates this process by learning features directly from the data.
• High performance: SSL-based models, like transformer-based architectures, have set new performance benchmarks in numerous NLP tasks.
• General-purpose representations: SSL in NLP learns generalized representations that are applicable to a plethora of downstream tasks.
• Resilience to noise: Given its predictive nature, SSL can handle noise and missing values in data, enhancing its robustness.
• Improved accuracy: The advanced models devised through SSL have shown improved accuracy and performance compared to older NLP methods.

• Computational cost: Training large-scale SSL models requires substantial computational resources, potentially limiting its accessibility for small-scale developers or organizations.
• Risk of overfitting: Given the complex predictive tasks, there is an inherent risk of overfitting if the model is not properly regularized.
• Data bias: Since SSL utilizes raw, unlabeled data from the web, it can inadvertently learn and propagate existing data biases. Implementing control mechanisms to curtail this is crucial.
• Opacity and tracking: SSL models, often of large size and complexity, can be challenging to interpret; tracking what the model has learned can be difficult.