Abstract: 

This tutorial targets AI researchers and practitioners who are interested in applying language processing techniques in cross-domain or cross-lingual tasks. I will provide the audience with a holistic view of (i) a wide selection of representation learning methods for text and multimedia data, (ii) techniques for aligning and transferring knowledge across multiple representations with limited supervision, and (iii) a wide range of applications using these techniques in natural language understanding. Participants will learn about recent trends and emerging challenges in this topic, representative tools and learning resources to obtain ready-to-use models, and how related models and techniques benefit real-world language-related AI applications.

Session Outline
Lesson 1: Introduction to representation learning in NLP
I will provide a general overview of embedding learning methods for encoding human language into machine-understandable format. On top of that, we discuss how domain and language-specific embedding spaces can be associated using retrofitting or joint learning methods.

Lesson 2: Cross-Lingual Transfer using multilingual representation in NLP
I will discuss how transferable representations are incorporated into various multilingual NLP tasks. We demonstrate how knowledge transfer allows NLP models trained on high-resource languages to be transferred to low-resource language tasks.


Lesson 3: Multimodal Representations and Transfer in NLP
I will demonstrate how the multimodal contextualized language representation models obtain signals from both text and images and help downstream models understand commonsense concepts in both human languages and vision.

Background Knowledge
I assume the audience has a basic understanding of deep learning and natural language processing although I will briefly introduce the deep learning architecture and the language processing tasks that this tutorial is based on.

Bio: 

Kai-Wei Chang is an assistant professor in the Department of Computer Science at the University of California Los Angeles (UCLA). His research interests include designing robust machine learning methods for large and complex data and building fair, reliable, and accountable language processing technologies for social good applications. Dr. Chang has published broadly in natural language processing, machine learning, and artificial intelligence. His research has been covered by news media such as Wires, NPR, and MIT Tech Review. His awards include the Sloan Research Fellowship (2021), the EMNLP Best Long Paper Award (2017), the KDD Best Paper Award (2010), and the Okawa Research Grant Award (2018). Dr. Chang obtained his Ph.D. from the University of Illinois at Urbana-Champaign in 2015 and was a post-doctoral researcher at Microsoft Research in 2016. Additional information is available at http://kwchang.net