论文标题
释义与核心：同一枚硬币的两个侧面
Paraphrasing vs Coreferring: Two Sides of the Same Coin
论文作者
论文摘要
我们研究了两个不同的NLP任务之间的潜在协同作用，既面对谓词词汇变异性：识别谓词释义和事件核心分辨率。首先，我们使用事件Coreference数据集中的注释作为遥远的监督，以重新攻击启发式提取的预鉴定解释。新得分通过原始评分方法在排名中平均获得了18分。然后，我们将相同的重新排列功能与最新事件核心分辨率模型的其他输入相同，该模型对模型的性能产生了适度但一致的改进。结果提出了一个有希望的方向，可以利用每个任务的数据和模型，以使其受益。
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.
