BERTwich: Extending BERT’s Capabilities to Model Dialectal and Noisy Text (2023.findings-emnlp)
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| Challenge: | Pre-trained language models like BERT deteriorate in the face of dialect variation or noise. |
| Approach: | They propose to sandwich BERT's encoder stack between additional encoder layers trained to perform masked language modeling on noisy text. |
| Outcome: | The proposed approach promotes zero-shot transfer to dialectal text and reduces embedding space between words and noisy counterparts. |
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| Challenge: | Recent studies show that fine-tuning pre-trained language models is unstable when there are only a small number of training samples available. |
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First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT (2021.eacl-main)
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| Challenge: | Multilingual pretrained language models have demonstrated remarkable zero-shot cross-lingual transfer capabilities. |
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| Challenge: | Pre-trained text encoders have advanced the state of the art on many NLP tasks . Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically . |
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| Challenge: | In addition, information on part-of-speech tagging is spread over different parts of the network and the pipeline might not be as neat as it seems. |
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| Challenge: | Among studies on localization of linguistic knowledge, it is unclear what information is encoded in each layer. |
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A Primer in BERTology: What We Know About How BERT Works (2020.tacl-1)
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| Challenge: | Pre-trained contextual language models have improved performance of many NLP tasks. |
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On the Nature of BERT: Correlating Fine-Tuning and Linguistic Competence (2022.coling-1)
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| Challenge: | Several studies on the interpretation of Neural Language Models (NLMs) focus on the linguistic generalization abilities of pre-trained models, but little attention is paid to how the linguistic knowledge of the models changes during fine-tuning. |
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