Automatic Generation of German Drama Texts Using Fine Tuned GPT-2 Models

8 Jan 2023 · Mariam Bangura, Kristina Barabashova, Anna Karnysheva, Sarah Semczuk, Yifan Wang ·

This study is devoted to the automatic generation of German drama texts. We suggest an approach consisting of two key steps: fine-tuning a GPT-2 model (the outline model) to generate outlines of scenes based on keywords and fine-tuning a second model (the generation model) to generate scenes from the scene outline. The input for the neural model comprises two datasets: the German Drama Corpus (GerDraCor) and German Text Archive (Deutsches Textarchiv or DTA). In order to estimate the effectiveness of the proposed method, our models are compared with baseline GPT-2 models. Our models perform well according to automatic quantitative evaluation, but, conversely, manual qualitative analysis reveals a poor quality of generated texts. This may be due to the quality of the dataset or training inputs.

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Adam • Attention Dropout • BPE • Cosine Annealing • Dense Connections • Discriminative Fine-Tuning • Dropout • GELU • GPT-2 • Layer Normalization • Linear Layer • Linear Warmup With Cosine Annealing • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • Softmax • Weight Decay

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Automatic Generation of German Drama Texts Using Fine Tuned GPT-2 Models

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