Heidi Christensen (University of Sheffield, UK)

Title to be announced.   (June 3, 2021, 15:00 CET)   

Summary:
Coming soon.

Jose Luis Alba Castro - Carmen García Mateo (University of Vigo)

Automatic Spanish Sign-Language Recognition: On-going Work & Challenges Ahead   (May 6, 2021, 15:00 CET)   

Summary:
In this talk we will quickly review the general approaches followed by the research community to solve the Sign Language Recognition (SLR) problem in the pre-deep learning era and then review, also briefly, the latest architectures using DNNs. These data-hungry models pose a very important problem in this specific task due to the scarcity of labeled data. In the last 5 years there has been a great deal of effort on compiling labeled datasets of Word-Level SLR and Continuous-SLR, but we are still very far from the amount of data readily available for other speech-based tasks. Acquiring SLR has the double challenge of needing donors that are scarce and needing SLR interpreters that help with the logistics, curation and labeling of the dataset. The GTM group at the atlanTTic Center in the University of Vigo has started this research line three years ago. We will show the state of the project nowadays and the state of the dataset we are acquiring with the help of Galician deaf associations and SL interpreters. We will also show the different approaches we are following both for understanding manual and facial components of the sign language and the latest results on Word-Level SLR. Video

Iryna Gurevych (Technische Universität Darmstadt)

Ricardo Baeza-Yates (Northeastern University)

Biases on Social Media (February 11th at 15:00 pm CET)

Summary:
Is social media data representative? If not, what are their biases? Can we mitigate those biases and make them representative? Does all this depend on the language? Can word embeddings help? We will answer partially all these questions with concrete use cases.
Video

Kyunghyun Cho (NYU)

Unreasonably Shallow Deep Learning (January 29, 2021, 17:30 CET) 

Summary:
The talk will be about some gotcha's in Deep Learning.
Video  PDF

Eduard Hovy (CMU)

The Birth of a New NLP Centre: Making the Most of a Newborn Technology (2020/11/05)     

Summary:
Natural Language Processing (NLP) is at a very exciting time in its history. In the last 5 years a new technology has revolutionized the way we do our work. Even without special adaptation it tends to work better than almost every prior method, and yet we still don't really know how it works! So this is also a dangerous time: how can you trust a system that might (and sometimes does) do very strange things for which you can find no explanation or correction? In such a situation it is not a bad idea to look at the history of NLP, what NLP is at its core, and how the new technology fits into the NLP landscape. And, most importantly, where NLP is going (with or without this new technology) and how we can best prepare for it. The HiTZ Centre has a wonderful opportunity to help shape a future in which NLP will be as ubiquitous and as useful as the cellphone.
Video