论文标题
基于多任务学习的神经桥接参考分辨率
Multi-task Learning Based Neural Bridging Reference Resolution
论文作者
论文摘要
我们提出了一个基于多个任务学习的神经模型，用于解决解决两个主要挑战的桥接参考。第一个挑战是缺乏带有桥接参考的大型语料库。为了解决这个问题，我们使用多任务学习来帮助以核心分辨率桥接参考分辨率。我们表明，最高晚上8点的实质性改善。可以通过此体系结构在完整的桥接分辨率上实现。第二个挑战是在不同语料库中使用的桥接的不同定义，这意味着使用专为一个语料库设计的特殊功能的手工编码系统或系统与其他语料库无法正常工作。我们的神经模型仅使用少量语料库独立特征，因此可以应用于不同的语料库。具有非常不同的桥接语料库的评估（Arrau，Isnotes，Bashi和SciCorp）表明，我们的架构在所有语料库中同样效果很好，并且在所有语料库中实现了SOTA的结果，全面的桥接分辨率均优于最佳报告的结果，最多可在36.3 pp .. p.p.p.中获得最佳报告的结果。
We propose a multi task learning-based neural model for resolving bridging references tackling two key challenges. The first challenge is the lack of large corpora annotated with bridging references. To address this, we use multi-task learning to help bridging reference resolution with coreference resolution. We show that substantial improvements of up to 8 p.p. can be achieved on full bridging resolution with this architecture. The second challenge is the different definitions of bridging used in different corpora, meaning that hand-coded systems or systems using special features designed for one corpus do not work well with other corpora. Our neural model only uses a small number of corpus independent features, thus can be applied to different corpora. Evaluations with very different bridging corpora (ARRAU, ISNOTES, BASHI and SCICORP) suggest that our architecture works equally well on all corpora, and achieves the SoTA results on full bridging resolution for all corpora, outperforming the best reported results by up to 36.3 p.p..
