Explainable AI (XAI) for text is an emerging field focused on developing novel techniques to render black-box models more interpretable for text-related tasks. To understand the recent advances in XAI for text, we have done an extensive literature review and user studies. Allowing users to easily explore the assets we created is a major challenge. In this demo we present an interactive website named XAIT. The core of XAIT is a tree-like taxonomy, with which the users can interactively explore and understand the field of XAI for text through different dimensions: (1) the type of text tasks in consideration; (2) the explanation techniques used for a particular task; (3) who are the target and appropriate users for a particular explanation technique. XAIT can be used as a recommender system for users to find out what are the appropriate and suitable explanation techniques for their text-related tasks, or for researchers who want to find out publications and tools relating to XAI for text.