| Challenge: | This tutorial will focus on NLP for endangered languages documentation and revitalization. |
| Approach: | This tutorial will focus on NLP for endangered languages documentation and revitalization . the goal is to motivate more NLP practitioners to work towards this important direction . |
| Outcome: | This tutorial will acquaint attendees with the process and the challenges of language documentation and revitalization. |
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How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language (2022.acl-long)
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| Challenge: | There are an estimated 6000 to 7000 spoken languages in the world, and at least 43% of them are endangered. |
| Approach: | They propose three principles that may help NLP practitioners foster mutual understanding and collaboration with language communities and three ways in which NLP can potentially assist in language education. |
| Outcome: | The proposed methods can be used to enrich Cherokee language resources with machine-in-the-loop processing and to provide language education. |
Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go (2022.acl-long)
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| Challenge: | ACL 2022 special Theme on "Language Diversity: from Low Resource to Endangered Languages" focuses on linguistic and sociopolitical challenges facing development of NLP technologies for African languages . |
| Approach: | They propose a typological framework for linguistic and sociopolitical challenges for NLP in African languages. |
| Outcome: | The main objective of this study is to motivate and advocate for an Afrocentric approach to technology development. |
Robustness and Adversarial Examples in Natural Language Processing (2021.emnlp-tutorials)
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| Challenge: | This tutorial aims to raise awareness of practical concerns about NLP robustness . it aims at addressing the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
| Approach: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it reviews recent studies on analyzing the weakness of NLP systems when facing adversarial inputs . |
| Outcome: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it will examine the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
Challenges and Strategies in Cross-Cultural NLP (2022.acl-long)
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Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Piqueras, Ilias Chalkidis, Ruixiang Cui, Constanza Fierro, Katerina Margatina, Phillip Rust, Anders Søgaard
| Challenge: | Various efforts have been made to accommodate linguistic diversity and serve speakers of many different languages. |
| Approach: | They propose a framework to examine cultural differences in NLP to better serve users . they argue that cultural knowledge, preferences and values can affect NLP practices . |
| Outcome: | The proposed framework examines how cultural knowledge, preferences and values can affect NLP practices. |
Beyond Paragraphs: NLP for Long Sequences (2021.naacl-tutorials)
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| Challenge: | In this tutorial, we will introduce document-level representation learning techniques . document-based learning is challenging due to the limited sequence length of many models . |
| Approach: | They will provide an overview of established long sequence NLP techniques and discuss memory-saving methods that are key to processing long sequences. |
| Outcome: | The tutorial will introduce the latest and ongoing techniques for document-level representation learning. |
Defining a New NLP Playground (2023.findings-emnlp)
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Sha Li, Chi Han, Pengfei Yu, Carl Edwards, Manling Li, Xingyao Wang, Yi Fung, Charles Yu, Joel Tetreault, Eduard Hovy, Heng Ji
| Challenge: | Recent explosion of performance of large language models (LLMs) has changed the field more abruptly and seismically than any other shift in the field’s 80 year history. |
| Approach: | They propose 20+ PhD-dissertation-worthy research directions to define a new NLP playground by combining theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
| Outcome: | The proposed research will cover theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
The Interplay between Metaphors and NLP (2026.acl-tutorials)
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| Challenge: | This tutorial will provide an overview of the metaphor processing field. |
| Approach: | This tutorial will provide an overview of the metaphor processing field . it will focus on recent directions opened by LLMs for metaphor interpretation . |
| Outcome: | The tutorial will discuss the influence of various metaphor theories on the creation of annotated resources and models. |
Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)
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| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
| Approach: | This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. |
| Outcome: | This tutorial will describe methods, applications, and open problems that have been developed and are being used to improve the quality and efficiency of synthetic data generation. |
Privacy-Preserving Natural Language Processing (2023.eacl-tutorials)
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| Challenge: | This tutorial will help the NLP community to get familiar with current research in privacy-preserving methods. |
| Approach: | This tutorial will help the NLP community to get familiar with current research in privacy-preserving methods. |
| Outcome: | The tutorial will cover membership inference, differential privacy, homomorphic encryption, or federated learning, all with typical use-cases and potential pitfalls. |