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Can pretrained language models (PLMs) generate derivationally complex words?We present the first study investigating this question, taking BERT as theexample PLM. We examine BERT's derivational capabilities in different settings,ranging from using the unmodified pretrained model to full finetuning. Our bestmodel, DagoBERT (Derivationally and generatively optimized BERT), clearlyoutperforms the previous state of the art in derivation generation (DG).Furthermore, our experiments show that the input segmentation crucially impactsBERT's derivational knowledge, suggesting that the performance of PLMs could befurther improved if a morphologically informed vocabulary of units were used.

DagoBERT: Generating Derivational Morphology with a Pretrained Language Model