CO-LOCATED WITH EMNLP IN SINGAPORE, DEC 7 2023

Orhan Firat, Google

Luke Zettlemoyer, Meta

Katharina Kann, UC Boulder

Multi-lingual representation learning methods have recently been found to be extremely efficient in learning features useful for transfer learning between languages and demonstrating potential in achieving successful adaptation of natural language processing (NLP) models into languages or tasks with little to no training resources. On the other hand, there are many aspects of such models which have the potential for further development and analysis in order to prove their applicability in various contexts. These contexts include different NLP tasks and also understudied language families, which face important obstacles in achieving practical advances that could improve the state-of-the-art in NLP of various low-resource or underrepresented languages.

This workshop aims to bring together the research community consisting of scientists studying different aspects in multilingual representation learning, currently the most promising approach to improve the NLP in low-resource or underrepresented languages, and provide the rapidly growing number of researchers working on the topic with a means of communication and an opportunity to present their work and exchange ideas. The main objectives of the workshop will be:

•    • To construct and present a wide array of multi-lingual representation learning methods, including their theoretical formulation and analysis, practical aspects such as the application of current state-of-the-art approaches in transfer learning to different tasks or studies on adaptation into previously under-studied context;
•    • To provide a better understanding on how the language typology may impact the applicability of these methods and motivate the development of novel methods that are more generic or competitive in different languages;
•    • To promote collaborations in developing novel software libraries or benchmarks in implementing or evaluating multi-lingual models that would accelerate progress in the field.

By allowing a communication means for research groups working on machine learning, linguistic typology, or real-life applications of NLP tasks in various languages to share and discuss their recent findings, our ultimate goal is to support rapid development of NLP methods and tools that are applicable to a wider range of languages.

Topics of interest include, but are not limited to:

   • Understanding the learning dynamics of multi-lingual representation learning methods

   • Multilingual pretraining for discriminative and generative downstream tasks

   • Probing and analysis of multilingual representations

   • New methods for multi-lingual representation learning

   • New approaches to language adaptation of NLP systems

   • Zero-shot and few-shot learning for multilingual NLP

   • Investigating and understanding transfer learning methods for adaptation of NLP systems into previously under-studied languages, such as morphologically-rich languages

   • Data sets, benchmarks or libraries for implementing and evaluating multi-lingual models

Research papers: We invite all potential participants to submit their novel research contributions in the related fields as long papers following the EMNLP 2023 long paper format (anonymized with 8 pages excluding the references, and an additional page for the camera-ready versions for the accepted papers). All accepted research papers will be published as part of our workshop proceedings and presented either as oral or poster presentations. Our research paper track will accept submissions through our own submission system available at MRL 2023 Softconf Submission.

Extended abstracts: Besides long paper submissions, we also invite previously published or ongoing and incomplete research contributions to our non-archival extended abstract track. All extended abstracts can use the same EMNLP template with a 2-page limit, excluding the bibliography.

MRL 2023 will feature a new shared task on Multi-task Multi-lingual Information Retrieval, which aims to provide a new evaluation benchmark for assessment of large scale representation learning models in a diverse set of under-represented languages in a range of predictive and generative tasks. We invite all interested peers in getting in touch about participation by joining mrl-shared-task-2023@googlegroups.com. More details will be available very soon.

   • Sep. 8, 2023: Paper Due Date

   • Oct. 6, 2023: Notification of Acceptance

   • Oct. 18, 2023: Camera-ready papers due

   • Dec. 7, 2023: Workshop

   • Dec. 8-10, 2023: Main conference

(All deadlines are 11.59 pm UTC -12h (“anywhere on Earth”))

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