Sentence Level Curriculum Learning for Improved Neural Conversational
  Models
Sean Paulsen
Designing machine intelligence to converse with a human user necessarily
requires an understanding of how humans participate in conversation, and thus
conversation modeling is an important task in natural language processing. New
breakthroughs in architecture and data gathering continue to push the
performance of such conversational AI models. However, designs neglect the
gradual buildup in sentence structure and complexity experienced by humans as
we learn to communicate. During training, our model accepts one or more
sentences as input and attempts to predict the next sentence in the
conversation one word at a time, so our goal is to separate training into
segments, with each segment's corpus comprised of longer sentence pairs than
the previous one. This will mimic the desired "buildup" component of human
learning. We begin with only "short" length sentence pairs, then only "medium"
length pairs, and so on. A majority of our experiments were toward optimizing
this technique, ensuring a proper representation of the technique's potential,
since many of the details were new questions. Our segment-trained models were
then able to achieve lower validation loss at the end of training than models
trained with standard text preparation. This segmented training is
straightforward to implement and our results provide a general direction for
future research to implement and improve it.