Papers with Quantifying contextualization
Quantifying the Contextualization of Word Representations with Semantic Class Probing (2020.findings-emnlp)
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| Challenge: | Pretrained language models are effective in solving NLP tasks, but there are still questions about how and why they work so well. |
| Approach: | They use BERT to quantify contextualization by studying the extent of inference . they show that top layer representations support highly accurate inference of semantic classes . |
| Outcome: | The proposed model is highly accurate, but weak in the lower layers . it is more task-specific after finetuning while lower layers are more transferable . |