  The goal of temporal relation extraction is to infer the temporal relation
between two events in the document. Supervised models are dominant in this
task. In this work, we investigate ChatGPT's ability on zero-shot temporal
relation extraction. We designed three different prompt techniques to break
down the task and evaluate ChatGPT. Our experiments show that ChatGPT's
performance has a large gap with that of supervised methods and can heavily
rely on the design of prompts. We further demonstrate that ChatGPT can infer
more small relation classes correctly than supervised methods. The current
shortcomings of ChatGPT on temporal relation extraction are also discussed in
this paper. We found that ChatGPT cannot keep consistency during temporal
inference and it fails in actively long-dependency temporal inference.
