NLP Course | For You

This is an extension to the (ML for) Natural Language Processing course I teach at the Yandex School of Data Analysis (YSDA) since fall 2018. For now, only part of the topics is likely to be covered here.

This new format of the course is designed for:

  • convenience
    Easy to find, learn or recap material (both standard and more advanced), and to try in practice.
  • clarity
    Each part, from front to back, is a result of my care not only about what to say, but also how to say and, especially, how to show something.
  • you
    I wanted to make these materials so that you (yes, you!) could study on your own, what you like, and at your pace. My main purpose is to help you enter your own very personal adventure. For you.


What's inside: A Guide to Your Adventure


Lectures-blogs

which I tried to make:

  • intuitive, clear and engaging;
  • complete: full lecture and more,
  • up-to-date with the field.

Bonus:

Seminars & Homeworks

For each topic, you can take notebooks from our 6.2k-☆ course repo.

From 2020, both PyTorch and Tensorflow!

Interactive parts & Exercises

Often I ask you to go over "slides" visualizing some process, play with something or just think.

Analysis and Interpretability

Since 2020, top NLP conferences (ACL, EMNLP) have the "Analysis and Interpretability" area: one more confirmation that analysis is an integral part of NLP.

Each lecture has a section with relevant results on internal workings of models and methods.


Research Thinking

Learn to think as a research scientist:

  • find flaws in an approach,
  • think why/when something can help,
  • come up with ways to improve,
  • learn about previous attempts.

It's well-known that you will learn something easier if you are not just given the answer right away, but if you think about it first. Even if you don't want to be a researcher, this is still a good way to learn things!

Demo: Research Card

Here I define the starting point: something you already know.

Here I ask you questions. Think (for a minute, a day, a week, ...) and then look at possible answers.
? Why this or that can be useful?
Possible answers
Here you will see some possible answers. This part is a motivation to try a new approach: usually, this is what a research project starts with.
? How can we use this to improve that model?
Existing solutions
Here I will summarize some previous attempts. You are not supposed to come up with something exactly like here - remember, each paper usually takes the authors several months of work. It's a habit of thinking about these things that counts: you have several ideas, you try; if they don't work, you think again. Eventually, something will work - and this is what papers tell you about.


Have Fun!

Just fun.

Here you'll see some NLP games related to a lecture topic.




Course

Word Embeddings

  • Distributional semantics
  • Count-based (pre-neural) methods
  • Word2Vec: learn vectors
  • GloVe: count, then learn
  • Evaluation: intrinsic vs extrinsic
  • Analysis and Interpretability
  • Bonus:

Seminar & Homework

Week 1 in the course repo.

Text Classification

  • Intro and Datasets
  • General Framework
  • Classical Approaches: Naive Bayes, MaxEnt (Logistic Regression), SVM
  • Neural Networks: RNNs and CNNs
  • Analysis and Interpretability
  • Bonus:

Seminar & Homework

Week 2 in the course repo.

Language Modeling

  • General Framework
  • N-Gram LMs
  • Neural LMs
  • Generation Strategies
  • Evaluating LMs
  • Practical Tips
  • Analysis and Interpretability
  • Bonus:

Seminar & Homework

Week 3 in the course repo.

read more ⇨

Seq2seq and Attention

  • Seq2seq Basics (Encoder-Decoder, Training, Simple Models)
  • Attention
  • Transformer
  • Subword Segmentation (e.g., BPE)
  • Inference (e.g., beam search)
  • Analysis and Interpretability
  • Bonus:

Seminar & Homework

Week 4 in the course repo.

Transfer Learning

  • What is Transfer Learning?
  • Pretrained Models (ELMO, BERT and other)
  • Adaptors
  • ...to be continued
  • Analysis and Interpretability
  • Bonus:

Seminar & Homework

Will appear in the course repo.

To be continued...



Supplementary

Convolutional Networks

  • Intuition
  • Building Blocks: Convolution (and parameters: kernel, stride, padding, bias)
  • Building Blocks: Pooling (max/mean, k-max, global)
  • CNNs Models: Text Classification
  • CNNs Models: Language Modeling
  • Analysis and Interpretability
To be continued...