Exploring Automatic Text Simplification of German Narrative Documents
In this paper, we apply transformer-based Natural Language Generation (NLG) techniques to the problem of text simplification. Currently, there are only a few German datasets available for text simplification, even fewer with larger and aligned documents, and not a single one with narrative texts. In this paper, we explore to which degree modern NLG techniques can be applied to German narrative text simplifications. We use Longformer attention and a pre-trained mBART model. Our findings indicate that the existing approaches for German are not able to solve the task properly. We conclude on a few directions for future research to address this problem.
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Methods
AdamW •
Attention Dropout •
Dense Connections •
Dilated Sliding Window Attention •
Dropout •
GELU •
Global and Sliding Window Attention •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
Longformer •
mBART •
Multi-Head Attention •
Residual Connection •
Scaled Dot-Product Attention •
Sliding Window Attention •
Softmax •
Weight Decay •
WordPiece
