| Challenge: | Existing and novel similarity measures are used to analyze contextual word representations . different architectures have rather similar representations, but different individual neurons. |
| Approach: | They propose a method to analyze contextual word representation models using similarity analysis. |
| Outcome: | The proposed approach can be used to analyze model similarity without external annotations. |
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| Challenge: | Existing work on learning contextual representations has used LSTM-based biLMs, but there is no reason to believe this is effective. |
| Approach: | They propose to use pre-trained bidirectional language models to learn contextual word embeddings for four NLP tasks and to use them to study the effects of architecture on endtask accuracy. |
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| Challenge: | Reading comprehension (RC) is a high-level task in natural language understanding that requires reading a document and answering questions about its content. |
| Approach: | They propose to provide a standard neural network for reading a document and answering a question about its content. |
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Deep Contextualized Word Representations (N18-1)
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Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer
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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. |
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Maria Lymperaiou, George Manoliadis, Orfeas Menis Mastromichalakis, Edmund G. Dervakos, Giorgos Stamou
| Challenge: | Recent advances in NLP research have focused on robustness and explainability issues of their evaluation strategies. |
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| Challenge: | Contextualized word representations are effective in downstream tasks such as question answering, named entity recognition, and sentiment analysis. |
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Probing Contextual Language Models for Common Ground with Visual Representations (2021.naacl-main)
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How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings (D19-1)
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| Challenge: | Existing word embeddings were static, requiring all senses of a polysemous word to share the same representation. |
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Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy (2021.acl-long)
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| Challenge: | Existing models that represent different senses of words in context are not accurate for polysemous words. |
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