论文标题
桥接图作为问题回答
Bridging Anaphora Resolution as Question Answering
论文作者
论文摘要
大多数关于桥接过敏分辨率的研究（Poesio等，2004; Hou等，2013b; Hou，2018a）使用成对模型来解决问题，并假设给出了黄金提及的信息。在本文中，我们将桥接图的解决方案作为基于上下文回答的问题。这使我们能够在不知道任何黄金信息的情况下找到给定的放电器的先决条件（放电本身除外）。我们为此任务提供了一个问题回答框架（BARQA），该框架利用了转移学习的力量。此外，我们提出了一种新的方法来生成大量“准桥式”训练数据。我们表明，我们的模型已在此数据集上进行了预训练，并以少量域数据集进行了微调，从而实现了在两个桥接Corpora上桥接Anaphora分辨率的新最新结果（ISNOTES（Markert等，2012）和Bashi（Roesiger，2018年））。
Most previous studies on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013b; Hou, 2018a) use the pairwise model to tackle the problem and assume that the gold mention information is given. In this paper, we cast bridging anaphora resolution as question answering based on context. This allows us to find the antecedent for a given anaphor without knowing any gold mention information (except the anaphor itself). We present a question answering framework (BARQA) for this task, which leverages the power of transfer learning. Furthermore, we propose a novel method to generate a large amount of "quasi-bridging" training data. We show that our model pre-trained on this dataset and fine-tuned on a small amount of in-domain dataset achieves new state-of-the-art results for bridging anaphora resolution on two bridging corpora (ISNotes (Markert et al., 2012) and BASHI (Roesiger, 2018)).
