Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2020 | SwissText/KONVENS | Conference |
ISSN | Citations | PageRank |
Proceedings of the 5th Swiss Text Analytics Conference and the
16th Conference on Natural Language Processing, SwissText/KONVENS 2020,
Zurich, Switzerland, June 23-25, 2020 [online only]. CEUR Workshop
Proceedings 2624 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karolina Zaczynska | 1 | 0 | 2.03 |
| Nils Feldhus | 2 | 0 | 1.01 |
| Robert Schwarzenberg | 3 | 36 | 4.08 |
| aleksandra gabryszak | 4 | 5 | 2.79 |
| Sebastian Möller | 5 | 877 | 141.17 |