Singapore

EMNLP 2023

TalaMT: Multilingual Machine Translation for Cabécar-Bribri-Spanish

chibchan

multilingual

low-resource

machine translation

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

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.

Improving Cross-Lingual Transfer for Open Information Extraction with Linguistic Feature Projection

Task-Based MoE for Multitask Multilingual Machine Translation

CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource Languages

Contrastive Learning for Universal Zero-Shot NLI with Cross-Lingual Sentence Embeddings

UD-MULTIGENRE – a UD-Based Dataset Enriched with Instance-Level Genre Annotations

Generating Continuations in Multilingual Idiomatic Contexts

PR-MCS: Perturbation Robust Metric for MultiLingual Image Captioning

Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs

A Joint Matrix Factorization Analysis of Multilingual Representations

X-SNS: Cross-Lingual Transfer Prediction through Sub-Network Similarity

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Improving Cross-Lingual Transfer for Open Information Extraction with Linguistic Feature Projection

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Neural Machine Translation Models with Back-Translation for the Extremely Low-Resource Indigenous Language Bribri

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