Singapore

EMNLP 2023

Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in text

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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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Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance

A Comprehensive Evaluation of Tool-Assisted Generation Strategies

Harnessing the power of LLMs: Evaluating human-AI text co-creation through the lens of news headline generation

InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text Generation

Ask To The Point: Open-Domain Entity-Centric Question Generation

NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark

Narrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport Reward

https://d3smihljt9218e.cloudfront.net/transcript/59358/subtitle/05f25fea6b827f4626168dba728354c1.vtt

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Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in text

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