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Evaluating German Transformer Language Models with Syntactic Agreement Tests

2020-07-07 · Karolina Zaczynska, Nils Feldhus, Robert Schwarzenberg, Aleksandra Gabryszak, Sebastian Möller

Pre-trained transformer language models (TLMs) have recently refashioned natural language processing (NLP): Most state-of-the-art NLP models now operate on top of TLMs to benefit from contextualization and knowledge induction. To explain their success, the scientific community conducted numerous analyses. Besides other methods, syntactic agreement tests were utilized to analyse TLMs. Most of the studies were conducted for the English language, however. In this work, we analyse German TLMs. To this end, we design numerous agreement tasks, some of which consider peculiarities of the German language. Our experimental results show that state-of-the-art German TLMs generally perform well on agreement tasks, but we also identify and discuss syntactic structures that push them to their limits.

📄 PDF Abstract BibTeX arXiv:2007.03765

Code (3)

DFKI-NLP/gevalm 공식 구현 pytorch
nfelnlp/nfelnlp
phueb/unmasked pytorch

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