  We study the potential synergy between two different NLP tasks, both
confronting predicate lexical variability: identifying predicate paraphrases,
and event coreference resolution. First, we used annotations from an event
coreference dataset as distant supervision to re-score heuristically-extracted
predicate paraphrases. The new scoring gained more than 18 points in average
precision upon their ranking by the original scoring method. Then, we used the
same re-ranking features as additional inputs to a state-of-the-art event
coreference resolution model, which yielded modest but consistent improvements
to the model's performance. The results suggest a promising direction to
leverage data and models for each of the tasks to the benefit of the other.
