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.

本文探讨了两种语言处理任务的潜在协同作用：识别谓词释义和事件共指解析。作者通过使用事件共指数据集的注释来重新评分启发式抽取的谓词释义，实现了 18 个平均精度点的提升，并将同样的重新排序方法应用于事件共指解析模型中，进一步提高了模型的性能。研究结果揭示了一种有效的利用数据和模型相互受益的途径。