Author: Matthias Aßenmacher

This book is the result of an experiment in university teaching. We were inspired by a group of other PhD Students around Christoph Molnar, who conducted another seminar on Interpretable Machine Learning in this format. Instead of letting every student work on a seminar paper, which more or less isolated from the other students, we wanted to foster collaboration between the students and enable them to produce a tangible outout (that isn’t written to spend the rest of its time in (digital) drawers). In the summer term 2020, some Statistics & Data Science Master students signed up for our seminar entitled “Modern Approaches in Natural Language Processing” and had (before kick-off meeting) no idea what they had signed up for: Having written an entire book by the end of the semester.

We were bound by the examination rules for conducting the seminar, but otherwise we could deviate from the traditional format. We deviated in several ways:

  1. Each student project is a chapter of this booklet, linked contentwise to other chapers since there’s partly a large overlap between the topics.
  2. We gave challenges to the students, instead of papers. The challenge was to investigate a specific impactful recent model or method from the field of NLP.
  3. We designed the work to live beyond the seminar.
  4. We emphasized collaboration. Students wrote the introduction to chapters in teams and reviewed each others individual texts.

Technical Setup

The book chapters are written in the Markdown language. The simulations, data examples and visualizations were created with R (R Core Team 2018). To combine R-code and Markdown, we used rmarkdown. The book was compiled with the bookdown package. We collaborated using git and github. For details, head over to the book’s repository.


R Core Team. 2018. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.