Singapore

EMNLP 2023

ChiWUG: A Graph-based Evaluation Dataset for Chinese Lexical Semantic Change Detection

lexical semantic change

detection

evaluation

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.

Literary Intertextual Semantic Change Detection: Application and Motivation for Evaluating Models on Small Corpora

Multi-lect automatic detection of Swadesh list items from raw corpus data in East Slavic languages

Changing usage of Low Saxon auxiliary and modal verbs

From Diachronic to Contextual Lexical Semantic Change: Introducing Semantic Difference Keywords (SDKs) for Discourse Studies

A longitudinal study about gradual changes in the Iranian Online Public Sphere pre and post of ‘Mahsa Moment’: Focusing on Twitter

GHisBERT – Training BERT from scratch for lexical semantic investigations across historical German language stages | VIDEO

Swap and Predict - Predicting the Semantic Changes in Words across Corpora by Context Swapping

Text normalization methods have been commonly applied to historical language or user-generated content, but less often to dialectal transcriptions. In this paper, we introduce dialect-to-standard normalization -- i.e., mapping phonetic transcriptions from different dialects to the orthographic norm of the standard variety -- as a distinct sentence-level character transduction task and provide a large-scale analysis of dialect-to-standard normalization methods. To this end, we compile a multilingual dataset covering four languages: Finnish, Norwegian, Swiss German and Slovene. For the two biggest corpora, we provide three different data splits corresponding to different use cases for automatic normalization. We evaluate the most successful sequence-to-sequence model architectures proposed for text normalization tasks using different tokenization approaches and context sizes. We find that a character-level Transformer trained on sliding windows of three words works best for Finnish, Swiss German and Slovene, whereas the pre-trained byT5 model using full sentences obtains the best results for Norwegian. Finally, we perform an error analysis to evaluate the effect of different data splits on model performance.

Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation

The Case for Scalable, Data-Driven Theory: A Paradigm for Scientific Progress in NLP

Causal NLP: A Path towards Opening the Black Box of NLP

keynote

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Literary Intertextual Semantic Change Detection: Application and Motivation for Evaluating Models on Small Corpora

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Next from EMNLP 2023

Literary Intertextual Semantic Change Detection: Application and Motivation for Evaluating Models on Small Corpora

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