I am glad to announce that on October 27 I will give a tech talk at Pi School.
Title: Interpreting Neural Language Models
Abstract: The field of Natural Language Processing (NLP) has seen an
unprecedented progress in the last few years. Much of this progress is
due to the replacement of traditional systems with newer and more
powerful algorithms based on neural networks and deep learning. This
improvement, however, comes at the cost of interpretability, since deep
neural models offer little transparency about their inner workings and
their abilities. Therefore, in the last few years, an increasingly large
body of work has been devoted to the analysis and interpretation of
these models.
This talk will be divided into two parts. In the first part, we will
briefly introduce Neural Language Models (NLMs) and the main techniques
developed for interpreting their decisions and their inner linguistic
knowledge. In the second part, we will see how to fine-tune one of the
most popular NLM and then analyze its decisions according to two
different interpretability methods: integrated gradients and analysis of
attention matrices.
The materials on the talk are available here: Slides and Code.