Triad-based Neural Network for Coreference Resolution
Triad-based Neural Network for Coreference Resolution
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发表时间：
2018-08
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通讯作者：
Yuanliang Meng;Anna Rumshisky
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作者：
Yuanliang Meng;Anna Rumshisky
我们提出了一个基于三元组的神经网络系统，产生的实体提到共指消解之间的亲和力分数。该系统同时接受三个提及作为输入，考虑到所有三个提及的相互依赖性和逻辑约束，从而比传统的成对方法做出更准确的预测。根据系统选择，亲和度分数可以进一步用于聚类或提及排名。我们的实验表明，一个标准的层次聚类使用的分数产生最先进的结果与MUC和B 3指标的英语部分的CoNLL 2012共享任务。该模型不依赖于许多手工制作的功能，易于训练和使用。三元组也可以很容易地扩展到更高阶的多元组。据我们所知，这是第一个神经网络系统模型的相互依赖性超过两个成员在提到的水平。
We propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. The system simultaneously accepts three mentions as input, taking mutual dependency and logical constraints of all three mentions into account, and thus makes more accurate predictions than the traditional pairwise approach. Depending on system choices, the affinity scores can be further used in clustering or mention ranking. Our experiments show that a standard hierarchical clustering using the scores produces state-of-art results with MUC and B 3 metrics on the English portion of CoNLL 2012 Shared Task. The model does not rely on many handcrafted features and is easy to train and use. The triads can also be easily extended to polyads of higher orders. To our knowledge, this is the first neural network system to model mutual dependency of more than two members at mention level.