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====== BERT ======

===== Introductions to BERT =====
  * Paper: [[https://arxiv.org/pdf/1810.04805.pdf|Devlin et al 2018 - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]]
  * Blogs
    * [[https://www.analyticsvidhya.com/blog/2019/09/demystifying-bert-groundbreaking-nlp-framework/| Demystifying BERT: A Comprehensive Guide to the Groundbreaking NLP Framework]]
    * [[http://jalammar.github.io/illustrated-bert/|The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)]]
  * Textbooks
    * [[https://web.stanford.edu/~jurafsky/slp3/11.pdf|SLP Ch 11]] (especially [[https://web.stanford.edu/~jurafsky/slp3/11.pdf#page=6|11.2]])
  * Training from scratch
    * [[https://aclanthology.org/2021.emnlp-main.831.pdf|Izsak et al 2021 - How to Train BERT with an Academic Budget]]
  * Retrospective Analyssis
    * [[https://arxiv.org/pdf/2306.02870.pdf|Nityasya et al 2023 - On “Scientific Debt” in NLP: A Case for More Rigour in Language Model Pre-Training Research]]

===== Extensions =====
  * [[https://arxiv.org/pdf/1902.04094.pdf|Wang & Cho 2019 - BERT has a Mouth, and It Must Speak:BERT as a Markov Random Field Language Model]] WARNING: Mistake in this paper, [[https://sites.google.com/site/deepernn/home/blog/amistakeinwangchoberthasamouthanditmustspeakbertasamarkovrandomfieldlanguagemodel|it's not an MRF]].
  * [[https://arxiv.org/pdf/2106.02736.pdf|Goyal et al 2021 - Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis–Hastings]]

===== Interpretation and Properties (BERTology) =====
Summary: [[https://arxiv.org/pdf/2002.12327.pdf|Rogers et al 2020 - A Primer in BERTology: What we know about how BERT works]].  See also [[ml:Neural Network Psychology]].
  * [[https://arxiv.org/pdf/1906.04341.pdf|2019 - What Does BERT Look At?An Analysis of BERT’s Attention]] Also points out that BERT looks at the SEP token as a no-op attention.
  * [[https://arxiv.org/pdf/1909.10430.pdf|Wiedemann et al 2019 - Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings]]
  * [[https://arxiv.org/pdf/1905.06316.pdf|Tenney et al 2019 - What do you learn from context? Probing for sentence structure in contextualized word representations]]
  * [[https://arxiv.org/pdf/1905.05950.pdf|Tenney et al 2019 - BERT Rediscovers the Classical NLP Pipeline]]
  * [[https://arxiv.org/pdf/2002.12327.pdf|Rogers et al 2020 - A Primer in BERTology: What we know about how BERT works]]
  * [[https://twitter.com/lvwerra/status/1485301457813487619?s=21|2022 - Visualization of position embeddings in BERT and GPT-2 (Twitter)]]
  * [[https://arxiv.org/pdf/2203.06204.pdf|Papadimitriou et al 2022 - When classifying grammatical role, BERT doesn’t care about word order... except when it matters]]



===== Applications =====
  * [[https://arxiv.org/pdf/1905.05583.pdf|Sun et al 2019 - How to Fine-Tune BERT for Text Classification?]] Exhaustive study investigating different  fine-tuning  methods  of  BERT  on  text classification tasks and provides a general strategy for BERT fine-tuning.
  * [[https://arxiv.org/pdf/1904.05255.pdf|Shi & Lin 2019 - Simple BERT Models for Relation Extraction and Semantic Role Labeling]]
  * [[https://arxiv.org/pdf/1903.10318.pdf|Liu 2019 - Fine-tune BERT for Extractive Summarization]]
  * [[https://arxiv.org/pdf/1904.09675.pdf|Zhang et al 2019 - BERTScore: Evaluating Text Generation with BERT]]

===== Domain & Language Variants =====
  * [[https://arxiv.org/pdf/2004.10220.pdf|Mulyar et al 2020 - MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning]]

===== Other Variants =====
  * [[https://arxiv.org/pdf/1909.05840.pdf|Shen et al 2019 - Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT]]
  * [[https://arxiv.org/pdf/1910.01108.pdf|Sanh et al 2019 - DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter]]

===== Related Pages =====
  * [[Pretraining]]
  * [[Transformers]]