Semanlink - [2002.12327] A Primer in BERTology: What we know about how BERT works

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Au sujet de ce document

sl:arxiv_author :
sl:arxiv_firstAuthor : Anna Rogers
sl:arxiv_num : 2002.12327
sl:arxiv_published : 2020-02-27T18:46:42Z
sl:arxiv_summary : Transformer-based models are now widely used in NLP, but we still do not understand a lot about their inner workings. This paper describes what is known to date about the famous BERT model (Devlin et al. 2019), synthesizing over 40 analysis studies. We also provide an overview of the proposed modifications to the model and its training regime. We then outline the directions for further research.@en
sl:arxiv_title : A Primer in BERTology: What we know about how BERT works@en
sl:arxiv_updated : 2020-02-27T18:46:42Z
sl:bookmarkOf : https://arxiv.org/abs/2002.12327
sl:creationDate : 2020-02-28
sl:creationTime : 2020-02-28T13:25:30Z

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Bookmark of: https://arxiv.org/abs/2002.12327

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[2003.05473] Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking (CoNNL 2019)

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Training BERT-base-uncased on English Wikipedia and then fine-tuned and evaluating it
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> BERT+Entity is a straightforward extension on top
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> an extreme
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### Related work

- > [Durrett and Klein (2014)](/doc/2020/01/a_joint_model_for_entity_analys) were the first to propose
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This paper uses neural techniques instead of CRF.

- > [Yamada](/showprop.do?pptyuri=http%3A%2F%2Fwww.semanlink.net%2F2001%2F00%2Fsemanlink-schema%23arxiv_author&pptyval=Ikuya%2BYamada) (2016, 2017) was the first to
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cf. [#Wikipedia2Vec](tag:wikipedia2vec). Compare with [newer work by Yamada](doc:2020/09/1909_01259_neural_attentive_b)

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