Singapore

EMNLP 2023

USB: A Unified Summarization Benchmark Across Tasks and Domains

factchecking

summarization

benchmark

**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

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

The Conversational Machine Reading Comprehension (CMRC) task aims to answer questions in conversations, which has been a hot research topic because of its wide applications. However, existing CMRC benchmarks in which each conversation is coupled with a static passage are inconsistent with real scenarios. In this regard, it is hard to evaluate model’s comprehension ability towards real scenarios. In this work, we propose the first Chinese CMRC benchmark Orca and further provide zero-
shot/few-shot settings to evaluate model’s generalization ability towards diverse domains. We collect 831 hot-topic driven conversations with 4,742 turns in total. Each turn of a conversation is assigned with a response-related passage, aiming to evaluate model’s comprehension ability more reasonably. The topics of conversations are collected from social media platform and cover 33 domains, trying to be consistent with real scenarios. Importantly, answers in Orca are all well-annotated natural responses rather than specific spans or short phrases in previous datasets. We implement two strong frameworks to tackle the challenge in Orca.
The results indicate there is substantial room for improvement for strong baselines such as ChatGPT on our CMRC benchmark.

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IRFL: Image Recognition of Figurative Language

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IRFL: Image Recognition of Figurative Language

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USB: A Unified Summarization Benchmark Across Tasks and Domains

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