Evaluating German Transformer Language Models with Syntactic Agreement Tests
This work addresses the gap in analyzing German language models for researchers, though it is incremental as it extends existing English-focused methods to German.
The paper tackled the problem of evaluating German transformer language models using syntactic agreement tests, finding that state-of-the-art models generally perform well but struggle with specific syntactic structures.
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.