Language Model
Dataset
Recommendation
Analysis
What Does BERT Look at? An Analysis of BERT’s Attention (TODO)
Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning
Computer Science Department, Stanford University
Facebook AI Research
ACL’19
- Link of Paper(Citation: 17)
- Link of Note
Method
A Neural Probabilistic Language Model
Yoshua Bengio, Rejean Ducharme, Pascal Vincent, Christian Jauvin Departement d’Informatique et Recherche Operationnelle JMLR’03
Efficient Estimation of Word Representations in Vector Space
Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
Google
Computer Science’13
Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean
Google
NIPS’13
GloVe: Global Vectors for Word Representation (TODO)
Jeffrey Pennington, Richard Socher, Christopher D. Mannning
Computer Science Department, Stanford University
EMNLP’14
- Link of Paper (Citation: 7,915)
- Link of Note
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer
Allen Institue for artificial Intelligence
ACL’18
Improving Language Understanding by Generative Pre-Training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever
OpenAI
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
Google AI Language
NAACL’19