While this may seem like a deviation from the original query, let me explain. In recent years, there has been an explosion in the use of transformers for natural language processing (NLP) tasks such as machine translation and question answering. These models have shown remarkable performance on various benchmarks, but they also come with some limitations. For example, they can be computationally expensive to train and run, which makes them less accessible to smaller organizations or individuals without access to large computing resources.
To address these challenges, researchers are exploring ways to make transformers more efficient and scalable while still maintaining their high performance. One approach is to use domain-specific GPT-3 functionality, as demonstrated in the first notebook we provided (Domain_Specific_GPT_3_Functionality.ipynb). This allows us to fine-tune a pretrained transformer model on specific tasks or datasets and achieve better results with fewer training steps.
Another approach is to use transfer learning techniques, as demonstrated in the second notebook we provided (KantaiBERT_Recommender.ipynb), which involves using a pretrained BERT model for recommendation systems. This allows us to leverage the power of transformers for tasks beyond traditional NLP applications and expand their scope into other domains such as e-commerce or finance.
In addition, we’re also exploring new architectures that combine convolutional layers with transformer blocks (Compact_Convolutional_Transformers.ipynb), which can improve the efficiency of transformers for tasks like image classification and object detection. These models are still in their early stages but show promising results on various benchmarks, suggesting that they may be a viable alternative to traditional convolutional neural networks (CNNs) for certain applications.

In terms of Semantic Role Labeling (SRL), this is a task that involves identifying the grammatical roles played by words in a sentence, such as subject, object, and verb. While transformers can perform SRL tasks to some extent, they still struggle with certain aspects like disambiguation or coreference resolution. To address these challenges, researchers are exploring ways to combine transformer-based models with other techniques like dependency parsing or named entity recognition (NER), which can provide additional context and improve the accuracy of SRL predictions.

However, if you’re specifically interested in Semantic Role Labeling (SRL) using ChatGPT or any other AI language model, I would recommend checking out Chapter 16: The Emergence of Transformer-Driven Copilots in “Transformers for NLP-2nd Edition”. This chapter discusses how transformer-based models can be used to improve the accuracy and efficiency of SRL tasks by combining them with other techniques like dependency parsing or named entity recognition (NER). It also provides examples and code snippets that demonstrate how these models can be implemented in practice.