| Challenge: | SciBERT is a pretrained language model based on BERT to improve performance on scientific NLP tasks. |
| Approach: | They propose a pretrained language model based on BERT to improve NLP performance . they evaluate on sequence tagging, sentence classification and dependency parsing . |
| Outcome: | The proposed model improves on sequence tagging, sentence classification and dependency parsing tasks with datasets from a variety of scientific domains. |
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| Challenge: | Existing language model pretraining methods do not capture dependencies or knowledge that span across documents. |
| Approach: | They propose a language model pretraining method that leverages links between documents . they use masked language modeling and document relation prediction to model LMs . |
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The Diminishing Returns of Masked Language Models to Science (2023.findings-acl)
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| Challenge: | Existing studies have shown that masked language models can improve downstream tasks by pretraining larger models for longer on more data. |
| Approach: | They empirically evaluate the extent to which these results extend to tasks in science by using 14 domain-specific transformer-based masked language models. |
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SciNLI: A Corpus for Natural Language Inference on Scientific Text (2022.acl-long)
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| Challenge: | Existing Natural Language Inference (NLI) datasets are not related to scientific text. |
| Approach: | They propose a large dataset for NLI that captures the formality in scientific text and contains 107,412 sentence pairs extracted from scholarly papers on NLP and computational linguistics. |
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)
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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou
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ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information (2021.acl-long)
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| Challenge: | ChineseBERT model incorporates glyph and pinyin information of Chinese characters into pretraining . proposed model achieves new performance boost over baseline models with fewer training steps . |
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Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains (2021.findings-acl)
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| Challenge: | Large pre-trained models suffer from domain shift and are not optimal for specific domains. |
| Approach: | They propose a general approach to developing small, fast and effective pretrained models for specific domains by adapting off-the-shelf general pretrained model and performing task-agnostic knowledge distillation in target domains. |
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HybridBERT - Making BERT Pretraining More Efficient Through Hybrid Mixture of Attention Mechanisms (2024.naacl-srw)
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| Challenge: | Pretrained transformer-based language models have produced state-of-the-art performance in most natural language understanding tasks. |
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FlauBERT: Unsupervised Language Model Pre-training for French (2020.lrec-1)
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Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoit Crabbé, Laurent Besacier, Didier Schwab
| Challenge: | Language models are a key step to achieve state-of-the-art results in many different Natural Language Processing (NLP) tasks. |
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DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains (2023.acl-long)
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Yanis Labrak, Adrien Bazoge, Richard Dufour, Mickael Rouvier, Emmanuel Morin, Béatrice Daille, Pierre-Antoine Gourraud
| Challenge: | Recent studies have shown that pre-trained language models improve performance on a wide range of NLP tasks. |
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NarrowBERT: Accelerating Masked Language Model Pretraining and Inference (2023.acl-short)
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| Challenge: | Large-scale language model pretraining is expensive as the models and pretraining corpora have become larger over time. |
| Approach: | They propose a modified transformer encoder that increases throughput for masked language model pretraining by more than 2x. |
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