  State-of-the-art attention based models, mostly centered around the
transformer architecture, solve the problem of sequence-to-sequence translation
using the so-called scaled dot-product attention. While this technique is
highly effective for estimating inter-token attention, it does not answer the
question of inter-sequence attention when we deal with conversation-like
scenarios. We propose an extension, HumBERT, that attempts to perform
continuous contextual argument generation using locally trained transformers.
