Singapore

EMNLP 2023

MUNI-NLP Systems for Low-resource Indic Machine Translation

indic

low-resource

machine translation

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.

GUIT-NLP's Submission to Shared Task: Low Resource Indic Language Translation

PRHLT's Submission to WLAC 2023

IACS-LRILT: Machine Translation for Low-Resource Indic Languages

Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets

PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India | VIDEO

SummIt: Iterative Text Summarization via ChatGPT | VIDEO

Improving the Robustness of Summarization Models by Detecting and Removing Input Noise

Enhancing Abstractiveness of Summarization Models through Calibrated Distillation

Factual Relation Discrimination for Factuality-oriented Abstractive Summarization | VIDEO

Inverse Reinforcement Learning for Text Summarization

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MUNI-NLP Systems for Low-resource Indic Machine Translation

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GUIT-NLP's Submission to Shared Task: Low Resource Indic Language Translation