Neural Language Models and Compositionality

In our first online event, Dr. Aleksandr Drozd presented Neural Language Models and Compositionality.

Aleksandr's abstract:

Natural language processing is a fast-growing field, with a rapid evolution of approaches and models. In the last 6 years, we have come a long way from word embeddings to contextualized representations to pre-trained transformers, with numerous success stories for NLP system performance on question answering, text classification, machine translation and other tasks.

Despite the successes, we are still very far from reliable verbal reasoning, and one of the unresolved issues is semantic compositionality. It is not only a practical challenge, but also a theoretical one, as there is still no consensus on what a compositional representation of morphologically complex word, phrase or a sentence should be like.

This talk  provides an introduction to our research endeavor in the field of NLP,  briefly introduces basics of NLP models and aspects of linguistic theory in which they are explicitly or implicitly grounded, particularly compositionality.

Thank you to Aleksandr, everyone who attended, and to our sponsor Cross Compass.

The full presentation is now on YouTube. Don't forget to subscribe!