representation learning

The 8th Workshop on Representation Learning for NLP (RepL4NLP 2023) will be hosted by ACL 2023 and held on 13/14 July 2023. The workshop is being organised by Burcu Can, Maximilian Mozes, Samuel Cahyawijaya, Naomi Saphra, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, and Chen Zhao; and advised by Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, and Laura Rimell. The workshop is organised by the ACL Special Interest Group on Representation Learning (SIGREP).

Dear all,

It is our great pleasure to invite you to the second edition of multilingual representation learning that will be held at EMNLP 2022 (as a hybrid WS) on December 8, 2022. The details regarding the paper submission and the shared task are given below:

The 7th Workshop on Representation Learning for NLP (RepL4NLP 2022) will be hosted by ACL 2022 and held on 26 May 2022.

*** Due to the recent surges of Covid-19 around the world, the organizers of the RepL4NLP-2021 workshop extended the paper submission deadline to May 3, 2021. Please see details below. ***

New Deadline: May 3, 2021

The 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), co-located with ACL 2021 in Bangkok, Thailand, invites papers of a theoretical or experimental nature describing recent advances in vector space models of meaning, compositionality, and the application of deep neural networks and spectral methods to NLP.

Deadline: April 26, 2021

The 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), co-located with ACL 2021 in Bangkok, Thailand, invites papers of a theoretical or experimental nature describing recent advances in vector space models of meaning, compositionality, and the application of deep neural networks and spectral methods to NLP.

We encourage submissions in the following topics, including but not limited to:

Deadline: April 26, 2021

The 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), co-located with ACL 2021 in Bangkok, Thailand, invites papers of a theoretical or experimental nature describing recent advances in vector space models of meaning, compositionality, and the application of deep neural networks and spectral methods to NLP.

We encourage submissions in the following topics, including but not limited to:

Deadline: April 26, 2021.

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Overview
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Publication of negative results is difficult in most fields, but in NLP the problem is exacerbated by the near-universal focus on improvements in benchmarks. This situation implicitly discourages hypothesis-driven research, and it turns creation and fine-tuning of NLP models into art rather than science. Furthermore, it increases the time, effort, and carbon emissions spent on developing and tuning models, as the researchers have no opportunity to learn what has already been tried and failed.

Exciting new learning conference, great NLP, speech, and ML invited speakers; innovative publication model; your participation encouraged! - Chris

Call for Papers:
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1st International Conference on Learning Representations (ICLR2013)
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Website: https://sites.google.com/site/representationlearning2013/
Held in conjunction with AISTATS2013, Scottsdale, Arizona, May 2nd-4th 2013