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 (from 2022, in Israel branch). 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.

Lectures-blogs with
Interactive parts & Exercises

Analysis and Interpretability

Seminars & Homeworks
notebooks in our 8.8k-☆ course repo

Research Thinking

learn to ask right questions Related Papers

with summaries and explanations Have Fun!

just fun

If you want to use the materials (e.g., figures) in your paper/report/whatnot and to cite this course, you can do this using the following BibTex:

        @misc{voita2020nlpCourse,

            title={ {NLP} {C}ourse {F}or {Y}ou},

            url={https://lena-voita.github.io/nlp_course.html},

            author={Elena Voita},

            year={2020},

            month={Sep}

        }

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.

Seminars & Homeworks

For each topic, you can take notebooks from our 8.8k-☆ 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.

Related Papers

Explore related work:

high-level: look at key results in short summaries and get an idea of what's going on in the field;
a bit deeper: for topics which interest you more, read longer summaries with illustrations and explanations;
in depth: read the papers you liked.

Demo: Paper Card

Good Author and Cool Author

Here I give you a couple of sentences explaining the high-level idea of the paper and/or its main results.

EMNLP 2019

More detals: click (yes, right here, right now!)

Here I give you a longer summary, with illustrations and explanations. I try to walk you through the authors' reasoning steps and key observations, and I try to make it as easy for you as possible. After you get the main idea, it'll be easier to read an original research paper.