FKIE_itf_2021 at CASE 2021 Task 1: Using Small Densely Fully Connected Neural Nets for Event Detection and Clustering

Nils Becker, Theresa Krumbiegel
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引用次数: 2

Abstract

In this paper we present multiple approaches for event detection on document and sentence level, as well as a technique for event sentence co-reference resolution. The advantage of our co-reference resolution approach, which handles the task as a clustering problem, is that we use a single neural net to solve the task, which stands in contrast to other clustering algorithms that often are build on more complex models. This means that we can set our focus on the optimization of a single neural network instead of having to optimize numerous different parameters. We use small densely connected neural networks and pre-trained multilingual transformer embeddings in all subtasks. We use either document or sentence embeddings, depending on the task, and refrain from using word embeddings, so that the implementation of complicated network structures and unfolding of RNNs, which can deal with input of different sizes, is not necessary. We achieved an average macro F1 of 0.65 in subtask 1 (i.e., document level classification), and a macro F1 of 0.70 in subtask 2 (i.e., sentence level classification). For the co-reference resolution subtask, we achieved an average CoNLL-2012 score across all languages of 0.83.
任务1:使用小型密集全连接神经网络进行事件检测和聚类
在本文中,我们提出了在文档和句子层面上的多种事件检测方法,以及一种事件句子共引用解析技术。我们的共同参考解决方法(将任务作为聚类问题处理)的优势在于,我们使用单个神经网络来解决任务,这与通常建立在更复杂模型上的其他聚类算法形成鲜明对比。这意味着我们可以将重点放在单个神经网络的优化上,而不必优化许多不同的参数。我们在所有子任务中使用小型密集连接的神经网络和预训练的多语言转换器嵌入。我们根据任务使用文档或句子嵌入,并且避免使用词嵌入,因此没有必要实现复杂的网络结构和展开可以处理不同大小输入的rnn。我们在子任务1(即文档级分类)中实现了0.65的平均宏F1,在子任务2(即句子级分类)中实现了0.70的宏F1。对于共同参考解析子任务,我们实现了所有语言的平均CoNLL-2012得分为0.83。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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