NLP Year In Review
Minimum viable reading
Minimum Viable Reading(MVR) is the least amount of study you need to stay updated with the industry. Select the verticals of your interest and read the latest surveys.
Faster transformers
Everybody is tired of the cost of working with transformers. This talk surveys the research happened to make them faster.
T3: High Performance Natural Language Processing
Better testing of NLP models
This talk by the author opens our eyes to better testing our models.
Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
Pre-trained models
I think this is your favourite topic. Isn’t it?
Pre-trained Models for Natural Language Processing: A Survey
Language Models are Few-Shot Learners
It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
Transfer Learning In NLP - Part 2
Smaller models
Distilling models will be a natural thing in the coming days until we discover a way to make great small models directly.
Speeding Up Transformer Training and Inference By Increasing Model Size
Knowledge Distillation: A Survey
A Survey of Model Compression and Acceleration for Deep Neural Networks
Domain adaptation
I think people give a whole lot less f**k about this important topic. I have a gut feeling that practitioners are hiding intentionally because this is where money gets printed.
Neural Unsupervised Domain Adaptation in NLP
Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
Neural search
I don’t want to say anything about this topic because I already said a lot.
Pretrained Transformers for Text Ranking: BERT and Beyond
(I am planning a new talk on the state of neural search. So you can skip reading this paper.)
Generative models
GPT3 makes everything possible. Isn’t it?
Evaluation of Text Generation: A Survey
Dealing with scarcity of data
Let us please give more fuck about this important topic. Send me cool papers if you know.
Revisiting Few-sample BERT Fine-tuning
Data Augmentation using Pre-trained Transformer Models
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?
Knowledge graphs
I hear KGs are going to be everywhere in the coming days.
Language Models are Open Knowledge Graphs
Extra
Visualisation
All in all
This has been a fantastic year for NLP. Did I miss out any influential paper or topic? Comment and let me know!
Enjoy the holidays!
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