Learning Rewards from Linguistic Feedback
  We explore unconstrained natural language feedback as a learning signal for
artificial agents. Humans use rich and varied language to teach, yet most prior
work on interactive learning from language assumes a particular form of input
(e.g., commands). We propose a general framework which does not make this
assumption, using aspect-based sentiment analysis to decompose feedback into
sentiment about the features of a Markov decision process. We then perform an
analogue of inverse reinforcement learning, regressing the sentiment on the
features to infer the teacher's latent reward function. To evaluate our
approach, we first collect a corpus of teaching behavior in a cooperative task
where both teacher and learner are human. We implement three artificial
learners: sentiment-based "literal" and "pragmatic" models, and an inference
network trained end-to-end to predict latent rewards. We then repeat our
initial experiment and pair them with human teachers. All three successfully
learn from interactive human feedback. The sentiment models outperform the
inference network, with the "pragmatic" model approaching human performance.
Our work thus provides insight into the information structure of naturalistic
linguistic feedback as well as methods to leverage it for reinforcement
learning.
