Course curriculum

  • 1

    The Next Thousand Languages

    • Abstract and Bio

    • The Next Thousand Languages
  • 2

    Continual Learning of Natural Language Processing Tasks

    • Abstract and Author Bio

    • Continual Learning of Natural Language Processing Tasks
  • 3

    Riding the Tailwind of NLP Explosion

    • Abstract and Author Bio

    • Riding the Tailwind of NLP Explosion
  • 4

    NLP Fundamentals

    • Abstract and Bio

    • NLP Fundamentals

Abstracts and Speaker

The Next Thousand Languages

In this talk we will present a new perspective on the language technology for all (LT4All) agenda, beginning with the structure of the world's linguistic diversity and the actual linguistic challenges on the ground. We will draw on experiences working in societies where there is no clear case for the popular practice of replicating human capabilities in translation or speech recognition, but where there are myriad other opportunities for language technologies. We will describe new ways of working with local communities, oral languages, and human-curated linguistic resources. The result is a radically inclusive approach to language technology which embraces and sustains linguistic diversity.

   Steven Bird, Professor @ Charles Darwin University

Continual Learning of Natural Language Processing Tasks

Continual learning or lifelong learning is an advanced machine learning paradigm that learns continuously, accumulates the learned knowledge, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation. Given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and to transfer the knowledge to future tasks. Continual learning breaks this isolated and closed-world learning paradigm to imitate the human lifelong learning capability. In this talk, I will first discuss some theory and then focus on continual learning of natural language processing tasks in both the pre-deployment mode and the post-deployment mode. 

   Bing Liu, PhD, Distinguished Professor @ University of Illinois

Riding the Tailwind of NLP Explosion

In this talk, we'll share how we modernized our NLP stack @ CBI R&D and the challenges we met with. Part I will walk you through the timeline and milestones of NLP evolution, highlighting significant trends after the "attention" revolution. Part II will discuss battle-ready lessons gained using transformer models across various tasks and languages, leveraging open source libraries such as HuggingFace Transformers and Pytorch Lightning. 


   Rongyao Huang, Lead Data Scientist @ CB Insights

NLP Fundamentals

In this course we will go through Natural Language Processing fundamentals, such as pre-processing techniques, tf-idf, embeddings, and more. It will be followed by practical coding examples, in python, to teach how to apply the theory to real use cases.

The goal of this workshop is to provide the attendees all the basic tools and knowledge they need to solve real problems and understand the most recent and advanced NLP topics.

   Leonardo De Marchi, VP of Labs @ Thomson Reuters

   Laura Skylaki, Manager of Applied Research @ Thomson Reuters Labs