no code implementations • 30 Apr 2024 • Minh Duc Bui, Fabian David Schmidt, Goran Glavaš, Katharina von der Wense
We further find that KD yields larger gains over pretraining from scratch when the data must be repeated under the fixed computation budget.
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.
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 • 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
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.