Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where? (2025.emnlp-main)
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| Challenge: | 20% of all papers in the ACL Anthology address social good issues . authors are more likely to do work addressing social good concerns when publishing in venues outside of ACL. |
| Approach: | They use author- and venue-level perspectives to map the landscape of NLP4SG . they find authors are more likely to do work addressing social good concerns outside of ACL . |
| Outcome: | The study analyzes the literature on NLP4SG and its impact on the ACL community . 20% of all papers in the anthology address social good issues, the study finds . |
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| Challenge: | Recent advances in natural language processing (NLP) have created a vast number of applications that are aimed at social good applications. |
| Approach: | They propose a dataset with three tasks that can help identify NLP4SG papers and characterize the NLP landscape by: (1) identifying the papers that address a social problem, (2) mapping them to the corresponding UN Sustainable Development Goals, and (3) identifying their methods. |
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| Challenge: | Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with many real-world applications. |
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| Challenge: | a new study examines the impact of NLP research published in top-tier conferences from 1979 to 2024 . language modeling has the widest internal and external influence, while linguistic foundations have lower impacts . |
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Examining Citations of Natural Language Processing Literature (2020.acl-main)
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| Challenge: | citations of NLP papers have decreased in recent years, but long papers get three times as many citation as short papers . citation data from the ACL Anthology and Google Scholar can be used to understand the field and quantify the impact of different types of papers. |
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Adithya V Ganesan, Siddharth Mangalik, Vasudha Varadarajan, Nikita Soni, Swanie Juhng, João Sedoc, H. Andrew Schwartz, Salvatore Giorgi, Ryan L Boyd
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| Challenge: | Current NLP models focus on information content while ignoring language’s social factors. |
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| Challenge: | Natural language processing (NLP) is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception. |
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| Challenge: | Google Scholar is the largest web search engine for academic literature and provides access to rich metadata associated with the papers. |
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We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields (2023.emnlp-main)
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| Challenge: | In this paper, we quantify the degree of influence between 23 fields of study and NLP (on each other) |
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