论文标题
核心解决任务的细分方法
Segmentation Approach for Coreference Resolution Task
论文作者
论文摘要
在核心解决方案中，重要的是要考虑一个核心集群的所有成员并立即决定所有成员。该技术可以帮助避免失去精确度，并在寻找长距离关系方面。介绍的论文是一份关于一个正在进行的想法的研究的报告，该研究提出了一种新的核心解决方法，该方法可以解决所有Coreference在一次通过中提到的所有Coreference提及。这是通过定义核心集群所有成员在文档中所有成员的位置并将其全部解决的嵌入方法来实现的。在提出的方法中，BERT模型已用于编码文档和旨在捕获嵌入式令牌之间的关系的头网络。然后将它们转换为嵌入矩阵的建议的跨度位置，该位置嵌入了文档中所有Coreference的位置。我们在Conll 2012数据集上测试了这个想法，尽管该方法的初步结果不完全满足最先进的结果，但它们是有希望的，并且可以比其他方法更好地捕获诸如长距离关系之类的功能。
In coreference resolution, it is important to consider all members of a coreference cluster and decide about all of them at once. This technique can help to avoid losing precision and also in finding long-distance relations. The presented paper is a report of an ongoing study on an idea which proposes a new approach for coreference resolution which can resolve all coreference mentions to a given mention in the document in one pass. This has been accomplished by defining an embedding method for the position of all members of a coreference cluster in a document and resolving all of them for a given mention. In the proposed method, the BERT model has been used for encoding the documents and a head network designed to capture the relations between the embedded tokens. These are then converted to the proposed span position embedding matrix which embeds the position of all coreference mentions in the document. We tested this idea on CoNLL 2012 dataset and although the preliminary results from this method do not quite meet the state-of-the-art results, they are promising and they can capture features like long-distance relations better than the other approaches.
