Papers by Severine Verlinden
Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish (2022.lrec-1)
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Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman, Fredrik Carlsson, Magnus Sahlgren
| Challenge: | a prerequisite for building large-scale generative models for other languages is access to large amounts of high-quality text data and powerful computational resources. |
| Approach: | They present a 3.5 billion parameter autoregressive language model, trained on a 100 GB Swedish corpus. |
| Outcome: | The proposed model performs well on a 100 GB Swedish corpus and is competent in comparison with existing models of similar size. |
Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution (2021.findings-acl)
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| Challenge: | Using unsupervised entity linking, we solve named entity recognition, coreference resolution and relation extraction tasks together. |
| Approach: | They propose to use a knowledge base to inject information into a joint IE model by using unsupervised entity linking. |
| Outcome: | The proposed model improves on two datasets with 5% F1 score. |
Fine-Grained Controllable Text Generation Using Non-Residual Prompting (2022.acl-long)
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| Challenge: | Existing approaches to control the text generation process are not expressive enough. |
| Approach: | They propose an encoder-decoder architecture that enables intermediate text prompts at arbitrary time steps. |
| Outcome: | The proposed architecture is expressive and versatile on multiple experimental settings. |