论文标题
Dagobert：使用验证的语言模型产生衍生形态
DagoBERT: Generating Derivational Morphology with a Pretrained Language Model
论文作者
论文摘要
预验证的语言模型（PLM）可以产生衍生化复杂的单词吗？我们提出了第一个研究这个问题的研究，以伯特为示例PLM。我们在不同的设置中检查了BERT的衍生能力，从使用未修改的预审预周化模型到完整的Finetuning。我们的最佳模型Dagobert（从衍生品和一般优化的BERT）显然优于先前的衍生化生成状态（DG）。此外，我们的实验表明，输入分割对BERT的派生知识产生至关重要的影响，这表明如果使用单位的形态学知情词汇，可以进一步提高PLM的性能。
Can pretrained language models (PLMs) generate derivationally complex words? We present the first study investigating this question, taking BERT as the example PLM. We examine BERT's derivational capabilities in different settings, ranging from using the unmodified pretrained model to full finetuning. Our best model, DagoBERT (Derivationally and generatively optimized BERT), clearly outperforms the previous state of the art in derivation generation (DG). Furthermore, our experiments show that the input segmentation crucially impacts BERT's derivational knowledge, suggesting that the performance of PLMs could be further improved if a morphologically informed vocabulary of units were used.
