Singapore

EMNLP 2023

DeepZensols: A Deep Learning Natural Language Processing Framework for Experimentation and Reproducibility

workshop paper

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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LaTeX Rainbow: Open Source Document Layout Semantic Annotation Framework from LaTeX to PDF

nerblackbox: A High-level Library for Named Entity Recognition in Python | VIDEO

News Signals: An NLP Library for Text and Time Series

PyTAIL: An Open Source Tool for Interactive and Incremental Learning of NLP Models with Human in the Loop for Online Data

The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation

torchdistill Meets Hugging Face Libraries for Reproducible, Coding-free Deep Learning Studies: A Case Study on NLP

Using Captum to Explain Generative Language Models

https://d3smihljt9218e.cloudfront.net/transcript/59318/subtitle/1584c977b6123aed025e0fdbee51b509.vtt

English (automatic)

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GPT4All: An Ecosystem of Open Source Compressed Language Models

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