  One of the long-term goals of artificial intelligence is to build an agent
that can communicate intelligently with human in natural language. Most
existing work on natural language learning relies heavily on training over a
pre-collected dataset with annotated labels, leading to an agent that
essentially captures the statistics of the fixed external training data. As the
training data is essentially a static snapshot representation of the knowledge
from the annotator, the agent trained this way is limited in adaptiveness and
generalization of its behavior. Moreover, this is very different from the
language learning process of humans, where language is acquired during
communication by taking speaking action and learning from the consequences of
speaking action in an interactive manner. This paper presents an interactive
setting for grounded natural language learning, where an agent learns natural
language by interacting with a teacher and learning from feedback, thus
learning and improving language skills while taking part in the conversation.
To achieve this goal, we propose a model which incorporates both imitation and
reinforcement by leveraging jointly sentence and reward feedbacks from the
teacher. Experiments are conducted to validate the effectiveness of the
proposed approach.
