Stefan Schweter


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Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0
Francesco De Toni | Christopher Akiki | Javier De La Rosa | Clémentine Fourrier | Enrique Manjavacas | Stefan Schweter | Daniel Van Strien
Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models

In this work, we explore whether the recently demonstrated zero-shot abilities of the T0 model extend to Named Entity Recognition for out-of-distribution languages and time periods. Using a historical newspaper corpus in 3 languages as test-bed, we use prompts to extract possible named entities. Our results show that a naive approach for prompt-based zero-shot multilingual Named Entity Recognition is error-prone, but highlights the potential of such an approach for historical languages lacking labeled datasets. Moreover, we also find that T0-like models can be probed to predict the publication date and language of a document, which could be very relevant for the study of historical texts.


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German’s Next Language Model
Branden Chan | Stefan Schweter | Timo Möller
Proceedings of the 28th International Conference on Computational Linguistics

In this work we present the experiments which lead to the creation of our BERT and ELECTRA based German language models, GBERT and GELECTRA. By varying the input training data, model size, and the presence of Whole Word Masking (WWM) we were able to attain SoTA performance across a set of document classification and named entity recognition (NER) tasks for both models of base and large size. We adopt an evaluation driven approach in training these models and our results indicate that both adding more data and utilizing WWM improve model performance. By benchmarking against existing German models, we show that these models are the best German models to date. All trained models will be made publicly available to the research community.


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FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP
Alan Akbik | Tanja Bergmann | Duncan Blythe | Kashif Rasul | Stefan Schweter | Roland Vollgraf
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models. The core idea of the framework is to present a simple, unified interface for conceptually very different types of word and document embeddings. This effectively hides all embedding-specific engineering complexity and allows researchers to “mix and match” various embeddings with little effort. The framework also implements standard model training and hyperparameter selection routines, as well as a data fetching module that can download publicly available NLP datasets and convert them into data structures for quick set up of experiments. Finally, FLAIR also ships with a “model zoo” of pre-trained models to allow researchers to use state-of-the-art NLP models in their applications. This paper gives an overview of the framework and its functionality. The framework is available on GitHub at .

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Towards Robust Named Entity Recognition for Historic German
Stefan Schweter | Johannes Baiter
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

In this paper we study the influence of using language model pre-training for named entity recognition for Historic German. We achieve new state-of-the-art results using carefully chosen training data for language models. For a low-resource domain like named entity recognition for Historic German, language model pre-training can be a strong competitor to CRF-only methods. We show that language model pre-training can be more effective than using transfer-learning with labeled datasets. Furthermore, we introduce a new language model pre-training objective, synthetic masked language model pre-training (SMLM), that allows a transfer from one domain (contemporary texts) to another domain (historical texts) by using only the same (character) vocabulary. Results show that using SMLM can achieve comparable results for Historic named entity recognition, even when they are only trained on contemporary texts. Our pre-trained character-based language models improve upon classical CRF-based methods and previous work on Bi-LSTMs by boosting F1 score performance by up to 6%.