| Challenge: | This tutorial will help researchers answer questions fundamental to the social sciences and humanities . |
| Approach: | This tutorial is designed to help researchers answer questions in the social sciences and humanities . it synthesizes recent computational techniques for handling and modeling temporal data . |
| Outcome: | The tutorial will synthesize recent techniques for handling and modeling temporal data, such as dynamic word embeddings, and identify useful tools for social scientists and digital humanities scholars. |
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| Challenge: | Languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves. |
| Approach: | They propose to categorise the types of change, the causes and the mechanisms underlying the different types of changes using large diachronic corpora and evaluation benchmarks. |
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Diachronic word embeddings and semantic shifts: a survey (C18-1)
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| Challenge: | Existing methods for tracing time-related semantic shifts with word embedding models lack the cohesion, common terminology and shared practices of more established areas of natural language processing. |
| Approach: | They propose several axes along which these methods can be compared and propose a framework for comparison. |
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Text-based inference of moral sentiment change (D19-1)
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| Challenge: | Existing work in NLP treats moral sentiment as a flat classification problem, but our framework probes moral sentiment change at multiple levels and captures moral dynamics concerning relevance, polarity, and finegrained categories informed by Moral Foundations Theory. |
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Understanding Computational Models of Semantic Change: New Insights from the Speech Community (2023.emnlp-main)
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| Challenge: | Using type-level and token-level word embeddings, we obtain semantic change estimates from type-based models and empirical linguistic properties. |
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Diachronic degradation of language models: Insights from social media (P18-2)
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| Challenge: | Existing studies have explored whether and how language models degrade over time, i.e. why they fail to work on contemporary language. |
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Findings of the Association for Computational Linguistics: ACL 2024 (2024.findings-acl)
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| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies (2021.acl-long)
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| Challenge: | linguistic and social aspects of code-switching are not discussed in the literature in linguistics. |
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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. |
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Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2025.acl-demo)
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| Challenge: | ACL 2025 System Demonstration Track accepted 64 papers based on reviews . short-listed 7 papers for Best System Demo award . |
| Approach: | the ACL 2025 System Demonstration Track is a conference for papers describing system demonstrations . the track received a record 187 submissions, of which 178 papers were valid with required materials . |
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Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) (2023.acl-demo)
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| Challenge: | 58 papers were selected for inclusion in the program, while a small number received only two reviews. |
| Approach: | the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023) will be held in london from July 9-14, 2023 . 58 submissions were selected for inclusion in the program, with an acceptance rate of 37%) |
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