The 5th Workshop on Representation Learning for NLP (RepL4NLP-2020) will be hosted at ACL 2020. The workshop is being organised by Spandana Gella, Johannes Welbl, Marek Rei, Fabio Petroni, Patrick Lewis, Emma Strubell, Minjoon Seo and Hannaneh Hajishirzi; and advised by Isabelle Augenstein, Kyunghyun Cho, Edward Grefenstette, Karl Moritz Hermann, and Chris Dyer. The workshop is organised by the ACL Special Interest Group on Representation Learning (SIGREP).
The 5th Workshop on Representation Learning for NLP aims to continue the success of the 1st Workshop on Representation Learning for NLP (about 50 submissions and over 250 attendees; second most attended collocated event at ACL'16 after WMT), 2nd Workshop on Representation Learning for NLP, 3rd Workshop on Representation Learning for NLP, and 4th Workshop on Representation Learning for NLP. The workshop was introduced as a synthesis of several years of independent *CL workshops focusing on vector space models of meaning, compositionality, and the application of deep neural networks and spectral methods to NLP. It provides a forum for discussing recent advances on these topics, as well as future research directions in linguistically motivated vector-based models in NLP.
Deadline for paper submission: April 17, 2020 Notification of acceptance: May 7, 2020 Camera ready submission due: May 18, 2020
- Early registration deadline (ACL 2020): June 26, 2020 11:59 EDT -- Register now!
- Workshop: July 9, 2020 PDT
All deadlines are 11:59 pm UTC -12h ("anywhere on Earth") unless otherwise specified.
- Kristina Toutanova, Google Research
- Ellie Pavlick, Brown University & Google
- Mike Lewis, Facebook AI Research
- Evelina Fedorenko, Massachusetts Institute of Technology
Following the rest of the ACL conference, RepL4NLP will be held remotely this year. To support the virtual format in a way that is as accessible as possible to attendees around the world, the workshop will be held in three sessions, each falling approximately within "normal" working hours for a different set of timezones. All talks will be pre-recorded and available to watch before and during the workshop, with live Q&A sessions for invited talks, and live poster sessions held on the workshop date of July 9. We're excited about this opportunity to make the workshop more accessible! Schedule now available.
- Compositionality with distributed representations and the role of syntax
- Analysis of language using eigenvalue, singular value and tensor decompositions
- Latent-variable and representation learning for language
- Neural networks and deep learning in NLP
- Training, evaluating, and applying representations
- Spectral learning and the method of moments in NLP
- Language models for different applications and understanding representations
- Multi-modal learning for distributional representations
- Knowledge base and graph embeddings