论文标题
隐喻检测的改进和扩展
Improvements and Extensions on Metaphor Detection
论文作者
论文摘要
隐喻在人类语言上无处不在。隐喻检测任务（MD）旨在检测和解释书面语言的隐喻，这在自然语言理解（NLU）研究中至关重要。在本文中，我们将基于预训练的变压器模型引入MD。在我们的评估中，我们的模型优于先前的最先进模型，F-1分数的相对提高从5.33％到28.39％。其次，我们将MD扩展到有关整个文本的隐喻性的分类任务，以使MD适用于更通用的NLU场景。最后，我们清理了一个MD基准数据集之一中的不正确或过时的注释，并使用基于变压器的模型重新基础。这种方法也可以应用于其他现有的MD数据集，因为这些基准数据集中的隐喻性注释可能已过时。未来的研究工作对于建立一个由较长和更复杂的文本组成的最新且宣布良好的数据集也是必要的。
Metaphors are ubiquitous in human language. The metaphor detection task (MD) aims at detecting and interpreting metaphors from written language, which is crucial in natural language understanding (NLU) research. In this paper, we introduce a pre-trained Transformer-based model into MD. Our model outperforms the previous state-of-the-art models by large margins in our evaluations, with relative improvements on the F-1 score from 5.33% to 28.39%. Second, we extend MD to a classification task about the metaphoricity of an entire piece of text to make MD applicable in more general NLU scenes. Finally, we clean up the improper or outdated annotations in one of the MD benchmark datasets and re-benchmark it with our Transformer-based model. This approach could be applied to other existing MD datasets as well, since the metaphoricity annotations in these benchmark datasets may be outdated. Future research efforts are also necessary to build an up-to-date and well-annotated dataset consisting of longer and more complex texts.
