  Hedging is a strategy for softening the impact of a statement in
conversation. In reducing the strength of an expression, it may help to avoid
embarrassment (more technically, ``face threat'') to one's listener. For this
reason, it is often found in contexts of instruction, such as tutoring. In this
work, we develop a model of hedge generation based on i) fine-tuning
state-of-the-art language models trained on human-human tutoring data, followed
by ii) reranking to select the candidate that best matches the expected hedging
strategy within a candidate pool using a hedge classifier. We apply this method
to a natural peer-tutoring corpus containing a significant number of
disfluencies, repetitions, and repairs. The results show that generation in
this noisy environment is feasible with reranking. By conducting an error
analysis for both approaches, we reveal the challenges faced by systems
attempting to accomplish both social and task-oriented goals in conversation.
