Workshop Description

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.

Main Topics

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


Duygu Ataman, NYU Hila Gonen, UW and Meta AI Sebastian Ruder, Google Gözde Gül Şahin, Koç University Genta Indra Winata, Bloomberg Arya McCarthy, Johns Hopkins University Omer Goldman, Bar-Ilan University Francesco Tinner, University of Amsterdam David Ifeoluwa Adelani, Deepmind and UCL Chris Emezue, TU Munich and MILA



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