A Review of XAI for Natural Language Processing
XAI, or Explainable Artificial Intelligence, is an emerging field that aims to make machine learning models more transparent and understandable. In the context of natural language processing (NLP), XAI can help improve NLP models’ interpretability, making it easier to understand how they work and why they make specific predictions and decisions.
One of the main challenges in NLP is that many models are based on complex neural networks, which can be difficult to understand and interpret. XAI techniques can help to make these models more transparent by providing explanations for the predictions they make. For example, an XAI-enabled NLP model might provide a list of the most important words or phrases that contributed to a particular prediction.
Another application of XAI in NLP is in the field of dialogue systems, this technology allows the creation of chatbots and virtual assistants that can interact with users in a human-like way. XAI can improve the interpretability of these systems by explaining the actions taken by the chatbot or virtual assistant. This can help improve the user experience by making it easier for users to understand how the system works and how it responds to their inputs.
A few examples of XAI techniques for NLP are:
- LIME (Local Interpretable Model-Agnostic…
