| Challenge: | Legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP) the legal system is experiencing an access to justice crisis, which could be partially alleviated with NLP. |
| Approach: | They argue that legal practitioners are slow to adopt natural language processing (NLP) they argue that there is a disconnect between legal needs and NLP research . |
| Outcome: | The proposed tasks bridge disciplinary disconnects and highlight interesting areas for legal NLP research that remain underexplored. |
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| Challenge: | Legal NLP holds the promise of improving access to justice and offers tools for empirical analysis of law on a large scale. |
| Approach: | They propose ways to think systematically about ethical limits of NLP . they place emphasis on three crucial normative parameters that have been underestimated . |
| Outcome: | The proposed methods are based on a real-life scenario that has prompted debate in the legal NLP community. |
On the Gap between Adoption and Understanding in NLP (2021.findings-acl)
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| Challenge: | a recent paper argues that current publications foster a gap between adoption and understanding of models . it also makes it easier to meet publication demands with method papers, argues the paper . |
| Approach: | They argue that current NLP publication models foster a gap between adoption and understanding of models . they argue that everlarger models make it harder to explain how our methods work . |
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A Major Obstacle for NLP Research: Let’s Talk about Time Allocation! (2022.emnlp-main)
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| Challenge: | Subpar time allocation has been a major obstacle for natural language processing research in recent years, argues a new position paper . |
| Approach: | They propose to identify the biggest traps the NLP community falls into and suggest solutions to solve them. |
| Outcome: | The authors outline multiple concrete problems together with their negative consequences and suggest remedies to improve the status quo. |
Is NLP Ready for Standardization? (2022.findings-emnlp)
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| Challenge: | a number of scientific fields, including telecommunications, networks and multimedia, lack standards in the field of NLP. |
| Approach: | They propose to examine how NLP lacks standards and how that can impact society, industry and regulations. |
| Outcome: | The proposed standards examine the needs of NLP researchers and industry . they argue that the lack of standards can impact the field, society and industry. |
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. |
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Understanding the Gap: an Analysis of Research Collaborations in NLP and Language Documentation (2025.findings-acl)
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| Challenge: | despite 20 years of NLP work, practical use of this work remains vanishingly scarce. |
| Approach: | They propose to use interviews and surveys to examine the lack of NLP adoption in LD . they find that linguists and language communities have little or no use of Nlp in their work . |
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Regulation and NLP (RegNLP): Taming Large Language Models (2023.emnlp-main)
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| Challenge: | polarization in AI safety and ethics debates are swaying political agendas on AI regulation and governance . regulation studies are rich source of knowledge on how to systematically deal with risk and uncertainty . |
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LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain (2023.findings-emnlp)
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| Challenge: | Recent advances in legal NLP have led to a rapid growth of the field . however, many benchmarks are available only in English and no multilingual benchmark exists . |
| Approach: | They propose to use 11 datasets covering 24 languages to compare NLP models. |
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The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges (2023.findings-acl)
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| Challenge: | Code-Switching is a common phenomenon in written text and conversation . it is not so common to observe code-switching in spoken language and not in written language . |
| Approach: | They present a systematic survey on code-switching research in natural language processing to understand the progress of the past decades and conceptualize the challenges and tasks on the topic. |
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A Legal Perspective on Training Models for Natural Language Processing (L18-1)
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| Challenge: | a significant concern in processing natural language data is the unclear legal status of the input and output data/resources. |
| Approach: | They examine which legal rules apply at relevant steps and how they affect the legal status of the results. |
| Outcome: | The proposed model training process is based on three scenarios . the analysis focuses on which legal rules apply and how they affect the legal status of the results . |