Natural Language Interaction with Explainable AI Models
  This paper presents an explainable AI (XAI) system that provides explanations
for its predictions. The system consists of two key components -- namely, the
prediction And-Or graph (AOG) model for recognizing and localizing concepts of
interest in input data, and the XAI model for providing explanations to the
user about the AOG's predictions. In this work, we focus on the XAI model
specified to interact with the user in natural language, whereas the AOG's
predictions are considered given and represented by the corresponding parse
graphs (pg's) of the AOG. Our XAI model takes pg's as input and provides
answers to the user's questions using the following types of reasoning: direct
evidence (e.g., detection scores), part-based inference (e.g., detected parts
provide evidence for the concept asked), and other evidences from
spatio-temporal context (e.g., constraints from the spatio-temporal surround).
We identify several correlations between user's questions and the XAI answers
using Youtube Action dataset.
