  Interpreting and understanding the predictions made by deep learning models
poses a formidable challenge due to their inherently opaque nature. Many
previous efforts aimed at explaining these predictions rely on input features,
specifically, the words within NLP models. However, such explanations are often
less informative due to the discrete nature of these words and their lack of
contextual verbosity. To address this limitation, we introduce the Latent
Concept Attribution method (LACOAT), which generates explanations for
predictions based on latent concepts. Our foundational intuition is that a word
can exhibit multiple facets, contingent upon the context in which it is used.
Therefore, given a word in context, the latent space derived from our training
process reflects a specific facet of that word. LACOAT functions by mapping the
representations of salient input words into the training latent space, allowing
it to provide latent context-based explanations of the prediction.
