Singapore

EMNLP 2023

The language of prompting: What linguistic properties make a prompt successful?

instruction-tuning

perplexity

prompting

zero-shot

robustness

**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.

workshop paper

USB: A Unified Summarization Benchmark Across Tasks and Domains

Figures of speech such as metaphors, similes, and idioms are integral parts of human communication. They are ubiquitous in many forms of discourse, allowing people to convey complex, abstract ideas and evoke emotion. As figurative forms are often conveyed through multiple modalities (e.g., both text and images), understanding multimodal figurative language is an important AI challenge, weaving together profound vision, language, commonsense and cultural knowledge.
In this work, we develop the Image Recognition of Figurative Language (IRFL) dataset. We leverage human annotation and an automatic pipeline we created to generate a multimodal dataset, and introduce two novel tasks as a benchmark for multimodal figurative language understanding. We experimented with state-of-the-art vision and language models and found that the best (22%) performed substantially worse than humans (97%). We release our dataset, benchmark, and code, in hopes of driving the development of models that can better understand figurative language.

IRFL: Image Recognition of Figurative Language

Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning

mReFinED: An Efficient End-to-End Multilingual Entity Linking System

Noisy Self-Training with Synthetic Queries for Dense Retrieval

Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval

Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens

Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features

Quantifying the Dialect Gap in Large Language Models and its Causes Across Languages

How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench

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USB: A Unified Summarization Benchmark Across Tasks and Domains

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The language of prompting: What linguistic properties make a prompt successful?

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USB: A Unified Summarization Benchmark Across Tasks and Domains

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The language of prompting: What linguistic properties make a prompt successful?

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