论文标题
通过变压器合奏对德国句子的自动可读性评估
Automatic Readability Assessment of German Sentences with Transformer Ensembles
论文作者
论文摘要
自动可读性评估的可靠方法有可能影响各种领域，从机器翻译到自我信息学习。最近，用于德语语言的大型语言模型（例如Gbert和GPT-2-Wechsel）已获得，从而可以开发基于深度学习的方法，有望进一步改善自动可读性评估。在这项贡献中，我们研究了微调Gbert和GPT-2-Wechsel模型的合奏能够可靠地预测德国句子的可读性的能力。我们将这些模型与语言特征相结合，并研究了预测性能对整体大小和组成的依赖性。 Gbert和GPT-2-Wechsel的混合合奏表现要比仅由Gbert或GPT-2-Wechsel模型组成的相同尺寸的合奏表现更好。我们的模型在2022年的Germeval 2022中进行了评估，该任务是关于德国句子数据的文本复杂性评估。在样本外数据上，我们的最佳集合达到了均方根误差为0.435。
Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0.435.
