Active Learning for Coreference Resolution using Discrete Annotation
Active Learning for Coreference Resolution using Discrete Annotation
复制标题
使用离散注释主动学习共指消解
DOI：
10.18653/v1/2020.acl-main.738
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Luke Zettlemoyer
中科院分区：
文献类型：
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作者：
Belinda Z. Li;Gabriel Stanovsky;Luke Zettlemoyer
我们改进了成对的注释，在共指消解的主动学习，通过要求注释者确定提及的先行词，如果提出的提及对被认为是不共指。这个简单的修改，当结合一个新的提及聚类算法来选择哪些例子来标记时，在每个注释预算获得的性能方面要有效得多。在现有基准共指数据集的实验中，我们表明，这个额外问题的信号导致每个人工注释小时的显着性能增益。未来的工作可以使用我们的注释协议，有效地开发新领域的共指模型。我们的代码是公开的。
We improve upon pairwise annotation for active learning in coreference resolution, by asking annotators to identify mention antecedents if a presented mention pair is deemed not coreferent. This simple modification, when combined with a novel mention clustering algorithm for selecting which examples to label, is much more efficient in terms of the performance obtained per annotation budget. In experiments with existing benchmark coreference datasets, we show that the signal from this additional question leads to significant performance gains per human-annotation hour. Future work can use our annotation protocol to effectively develop coreference models for new domains. Our code is publicly available.