Singapore

EMNLP 2023

IndiSocialFT: Multilingual Word Representation for Indian languages in code-mixed environment

code-mixed multilingual

word embedding model

indian languages

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.

Given the complexity of the judiciary in the US Supreme Court, various procedures, along with various resources, contribute to the court system. However, most research focuses on a limited set of resources, e.g., court opinions or oral arguments, for analyzing a specific perspective in court, e.g., partisanship or voting. To gain a fuller understanding of these perspectives in the legal system of the US Supreme Court, a more comprehensive dataset, connecting different sources in different phases of the court procedure, is needed. To address this gap, we present a multi-sourced dataset for the Supreme Court, comprising court resources from different procedural phases, connecting language documents with extensive metadata. We showcase its utility through a case study on how different court documents reveal the decision direction (conservative vs. liberal) of the cases. We analyze performance differences across three protected attributes, indicating that different court resources encode different biases, and reinforcing that considering various resources provides a fuller picture of the court procedures. We further discuss how our dataset can contribute to future research directions.

Super-SCOTUS: A multi-sourced dataset for the Supreme Court of the US

Large Language Models for Legally Enforceable Hate Speech Detection

More than Votes? Voting and Language based Partisanship in the US Supreme Court

LLMs -- the Good, the Bad or the Indispensable?: A Use Case on Legal Statute Prediction and Legal Judgment Prediction on Indian Court Cases

Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer? | VIDEO

Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction

Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning

A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction

Legal Passage Retrieval: A pragmatic approach to legal AI

LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain

technical paper

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Next from EMNLP 2023

Super-SCOTUS: A multi-sourced dataset for the Supreme Court of the US

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Next from EMNLP 2023

Super-SCOTUS: A multi-sourced dataset for the Supreme Court of the US

Downloads