Overview

The 4th Workshop on Representation Learning for NLP (RepL4NLP) will be held on TBA 2019, and hosted by an ACL conference. The workshop is being organised by Isabelle Augenstein, Spandana Gella, Sebastian Ruder, Katharina Kann, Burcu Can, Alexis Conneau, Johannes Welbl, Xian Ren and Marek Rei; and advised by Kyunghyun Cho, Edward Grefenstette, Karl Moritz Hermann, Chris Dyer and Laura Rimell. The workshop is organised by the ACL Special Interest Group on Representation Learning (SIGREP).

The 4th 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) and 2nd Workshop on Representation Learning for NLP, which were 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.

Key Dates

  • Deadline for paper submission: TBA
  • Notification of acceptance: TBA
  • Camera ready submission due: TBA
  • Early registration deadline: TBA
  • Workshop: TBA 2018

Keynote Speakers

TBA. Last year's speakers's were:

Discussion Panel Members

TBA. Last year's panel members were:

Topics

  • Distributional compositional semantics
  • Analysis of language using eigenvalue, singular value and tensor decompositions
  • Latent-variable and representation learning for language
  • Neural networks and deep learning in NLP
  • Word embeddings and their applications
  • Spectral learning and the method of moments in NLP
  • Language modeling for automatic speech recognition, statistical machine translation, and information retrieval
  • The role of syntax in compositional models
  • Language modeling for logical and natural reasoning
  • Integration of distributional representations with other models
  • Multi-modal learning for distributional representations
  • Knowledge base and graph embeddings

Contact: repl4nlp@googlegroups.com