Singapore

EMNLP 2023

OPUS-CAT Terminology Systems for the WMT23 Terminology Shared Task

opus-cat

terminology

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.

Semantically-Informed Regressive Encoder Score

RoCS-MT: Robustness Challenge Set for Machine Translation

Linguistically Motivated Evaluation of the 2023 State-of-the-art Machine Translation: Can ChatGPT Outperform NMT?

We introduce the submissions of the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task. Our team submitted predictions for the English-German language pair on all two sub-tasks: (i) sentence- and word-level quality prediction; and (ii) fine-grained error span detection. This year, we further explore pseudo data methods for QE based on NJUQE framework\footnote{\url{https://github.com/NJUNLP/njuqe}}. We generate pseudo MQM data using parallel data from the WMT translation task. We pre-train the XLMR large model on pseudo QE data, then fine-tune it on real QE data. At both stages, we jointly learn sentence-level scores and word-level tags. Empirically, we conduct experiments to find the key hyper-parameters that improve the performance. Technically, we propose a simple method that covert the word-level outputs to fine-grained error span results. Overall, our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks by a considerable margin.

Unify Word-level and Span-level Tasks: NJUNLP's Participation for the WMT2023 Quality Estimation Shared Task

MUNI-NLP Submission for Czech-Ukrainian Translation Task at WMT23

Trained MT Metrics Learn to Cope with Machine-translated References | VIDEO

Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting

MUNI-NLP Systems for Low-resource Indic Machine Translation

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

PRHLT's Submission to WLAC 2023

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OPUS-CAT Terminology Systems for the WMT23 Terminology Shared Task

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