no code implementations • IJCNLP 2019 • Fabian David Schmidt, Markus Dietsche, Simone Paolo Ponzetto, Goran Glava{\v{s}}
We introduce Seagle, a platform for comparative evaluation of semantic text encoding models on information retrieval (IR) tasks.
1 code implementation • Proceedings of the Conference on Empirical Methods in Natural Language Processing 2022 • Fabian David Schmidt, Ivan Vulić, Goran Glavaš
Large multilingual language models generally demonstrate impressive results in zero-shot cross-lingual transfer, yet often fail to successfully transfer to low-resource languages, even for token-level prediction tasks like named entity recognition (NER).
Multilingual text classification named-entity-recognition +3
1 code implementation • 26 May 2023 • Fabian David Schmidt, Ivan Vulić, Goran Glavaš
The results indicate that averaging model checkpoints yields systematic and consistent performance gains across diverse target languages in all tasks.
1 code implementation • 16 Oct 2023 • Fabian David Schmidt, Ivan Vulić, Goran Glavaš
Because of this, model selection based on source-language validation is unreliable: it picks model snapshots with suboptimal target-language performance.