The 7th Workshop on Representation Learning for NLP (RepL4NLP 2022) will be hosted by ACL 2022 and held on 26 May 2022.
The workshop aims to continue the success of the RepL4NLP workshop series, with the 1st Workshop on Representation Learning for NLP having received about 50 submissions and over 250 attendees -- the second most attended collocated event at ACL'16 after WMT. 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. The workshop will take place in a hybrid setting, and, as in previous years, features interdisciplinary keynotes, paper presentations, posters, as well as a panel discussion.
Topics
- Developing new representations: at the document, sentence, word, or sub-word level, using language model objectives, word embeddings, spectral methods, etc.
- Evaluating existing representations: probing representations for generalization, compositionality & robustness, adversarial evaluation, analysis of representations.
- Efficient learning of representations and inference: with respect to training and inference time, model size, amount of training data, etc.
- Beyond English / text representations: multi-modal, cross-lingual, knowledge-informed embeddings, structure-informed embeddings (syntax, morphology), etc.
Key Dates
- Deadline for paper submission: Feb 28th, 2022
- Notification of acceptance: March 26th, 2022
- Camera ready submission due: April 10th, 2022
- Early registration deadline: TBA
Keynote Speakers
- Been Kim (Google Brain)
- Emma Strubell (Carnegie Mellon University)
- Monojit Choudhury (Microsoft Research, India)
- Percy Liang (Stanford University)
- Sebastian Riedel (University College London & Facebook AI Research)
Organizers
- Spandana Gella (Amazon AI)
- He He (New York University)
- Burcu Can (University of Wolverhampton)
- Maximilian Mozes (University College London)
- Eleonora Giunchiglia (University of Oxford)
- Sewon Min (University of Washington)
- Samuel Cahyawijaya (Hong Kong University of Science and Technology)
- Xiang Lorraine Li (University of Massachusetts Amherst)
- Bodhisattwa Prasad Majumder (University of California San Diego)
Senior Advisors
- Isabelle Augenstein (University of Copenhagen)
- Anna Rogers (University of Copenhagen)
- Kyunghyun Cho (New York University)
- Edward Grefenstette (Facebook AI Research)
- Chris Dyer (DeepMind)
- Laura Rimell (DeepMind)
The workshop is organized by the ACL Special Interest Group on Representation Learning (SIGREP).