Papers by Stefan Schweter
FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP (N19-4)
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| Challenge: | Existing approaches combine word embeddings with character-level features to model additional features such as subword structures and meaning ambiguity. |
| Approach: | They present FLAIR, an NLP framework that enables embeddings of word and document data . they propose a hierarchical learning architecture that concatenates output states of a character-level CNN or RNN with the output states from a task data. |
| Outcome: | The proposed framework hides embedding-specific engineering complexity and allows researchers to "mix and match" various embeddables with little effort. |
German’s Next Language Model (2020.coling-main)
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| Challenge: | In this paper we compare the performance of our deep transformer based language models to existing models. |
| Approach: | They present a set of BERT and ELECTRA based German language models, GBERT and GELECTRE. |
| Outcome: | The proposed models outperform the previous best models on NER and classification tasks but are prohibitively large for many. |