  Intelligent robots designed to interact with humans in real scenarios need to
be able to refer to entities actively by natural language. In spatial referring
expression generation, the ambiguity is unavoidable due to the diversity of
reference frames, which will lead to an understanding gap between humans and
robots. To narrow this gap, in this paper, we propose a novel
perspective-corrected spatial referring expression generation (PcSREG) approach
for human-robot interaction by considering the selection of reference frames.
The task of referring expression generation is simplified into the process of
generating diverse spatial relation units. First, we pick out all landmarks in
these spatial relation units according to the entropy of preference and allow
its updating through a stack model. Then all possible referring expressions are
generated according to different reference frame strategies. Finally, we
evaluate every expression using a probabilistic referring expression resolution
model and find the best expression that satisfies both of the appropriateness
and effectiveness. We implement the proposed approach on a robot system and
empirical experiments show that our approach can generate more effective
spatial referring expressions for practical applications.
