Singapore

EMNLP 2023

Kani: A Lightweight and Highly Hackable Framework for Language Model Applications | VIDEO

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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Antarlekhaka: A Comprehensive Tool for Multi-task Natural Language Annotation

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

GPT4All: An Ecosystem of Open Source Compressed Language Models

GPTCache: An Open-Source Semantic Cache for LLM Applications Enabling Faster Answers and Cost Savings

Improving NER Research Workflows with SeqScore | VIDEO

https://d3smihljt9218e.cloudfront.net/transcript/59315/subtitle/e7a5e55fb3e44fcfeecd212ead51a545.vtt

English (automatic)

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Kani: A Lightweight and Highly Hackable Framework for Language Model Applications | VIDEO

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nanoT5: Fast & Simple Pre-training and Fine-tuning of T5 Models with Limited Resources

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