| Challenge: | Recent successful models for document-level understanding have used hierarchical encoding and CRFs to capture dependencies between subsequent labels. |
| Approach: | They propose a pretrained language model that captures contextual dependencies without hierarchical encoding nor a CRF. |
| Outcome: | The proposed model captures contextual dependencies without hierarchical encoding nor a CRF on four datasets, including a new dataset of structured scientific abstracts. |
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Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
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| Challenge: | Pre-trained contextual representations like BERT have been widely used for NLP tasks. |
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| Challenge: | aims to benchmark recent progress in language understanding models that output contextualised representations at the character level. |
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Which *BERT? A Survey Organizing Contextualized Encoders (2020.emnlp-main)
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| Challenge: | a survey on language representation learning aims to highlight common themes . we focus on the areas of progress, compared to other fields, and discuss how each area is evaluated. |
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Pre-trained language model representations for language generation (N19-1)
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Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith
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| Challenge: | Recent advances in language modeling have made it viable to model language as distributions over characters. |
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