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Workshop
Efficient Natural Language and Speech Processing (Models, Training, and Inference)
Mehdi Rezaghoizadeh · Lili Mou · Yue Dong · Pascal Poupart · Ali Ghodsi · Qun Liu

Mon Dec 13 05:00 AM -- 05:00 PM (PST) @
Event URL: https://neurips2021-nlp.github.io/ »

This workshop aims at introducing some fundamental problems in the field of natural language and speech processing which can be of interest to the general machine learning and deep learning community to improve the efficiency of the models, their training and inference. The workshop program offers an interactive platform for gathering experts and talents from academia and industry through different invited keynote talks, panel discussions, paper submissions, reviews, posters, oral presentations and a mentorship program.
This will provide an opportunity to discuss and learn from each other, exchange ideas, build connections, and brainstorm on potential solutions and future collaborations. The topics of this workshop can be of interest for people working on general machine learning, deep learning, optimization, theory and NLP & Speech applications.

Call for Papers
We encourage the NeurIPS community to submit their solutions, ideas, and ongoing work concerning data, model, training, and inference efficiency for NLP and speech processing. The scope of this workshop includes, but not limited to, the following topics.
(For more details please visit the Workshop Homepage.)

- Efficient Pre-Training and Fine-Tuning
- Model Compression
- Efficient Training
- Data Efficiency
- Edge Intelligence

Important Dates:
- Submission Deadline: September 18, 2021 (AOE)
- Acceptance Notification: October 22, 2021
- Camera-Ready Submission: November 1, 2021
- Workshop Date: December 13, 2021

Author Information

Mehdi Rezaghoizadeh (Huawei Technologies)
Lili Mou (University of Alberta)
Yue Dong (McGill)
Pascal Poupart (University of Waterloo & Vector Institute)
Ali Ghodsi (University of Waterloo)
Qun Liu (Huawei Noah's Ark Lab)

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