| Challenge: | a glass ceiling exists within the field of NLP, but no study has examined this issue . female representation in Computer Science is lower than the average STEM field . |
| Approach: | They propose to use a mathematical model to show that a glass ceiling exists in NLP . they find that there is a growing mentor gender gap and a disparity between mentors . |
| Outcome: | The proposed model shows that a glass ceiling exists within the field of NLP since the mid 2000s. |
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A Systematic Review of Reproducibility Research in Natural Language Processing (2021.eacl-main)
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| Challenge: | Despite the recent progress in reproducibility, the field is far from reaching a consensus on how reproducibility should be defined, measured and addressed. |
| Approach: | They propose to provide a wide-angle snapshot of current work on reproducibility in NLP. |
| Outcome: | The proposed work will provide a wide-angle snapshot of current work on reproducibility in NLP. |
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 . |
| Outcome: | The authors argue that current publications foster a gap between adoption and understanding of models . they argue that the rise of everlarger models makes it harder to explain how our methods work . |
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. |
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. |
Should We Ban English NLP for a Year? (2022.emnlp-main)
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| Challenge: | aaron carroll: two thirds of NLP research is devoted to developing technology for speakers of English . carroll says this bias feeds into consumer technologies to widen existing inequality gaps . he says we need to consider more concrete measures to mitigate climate change . |
| Approach: | a new paper argues that NLP is contributing to global inequalities through a digital language divide . a carbon tax, cap-and-trade and car-free Sundays are examples of measures to mitigate climate change . |
| Outcome: | a new paper argues that NLP is contributing to global inequalities through a digital language divide . a carbon tax, cap-and-trade and car-free Sundays are examples of measures to mitigate climate change . |
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019) (D19-61)
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| Challenge: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . |
| Approach: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . call for papers for this second workshop met with a strong response . |
| Outcome: | the EMNLP-IJCNLP 2019 workshop on deep learning approaches for low-resource natural language processing takes place in Hong Kong, China. |
A Primer in BERTology: What We Know About How BERT Works (2020.tacl-1)
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| Challenge: | a new study examines the current state of knowledge about the BERT model . the model is a stack of transformer encoder layers that are based on multiple self-attention ''heads'' |
| Approach: | They present a survey of over 150 studies of the popular Transformer-based model BERT . they discuss the current state of knowledge about how BERT works and how it is represented . |
| Outcome: | The proposed model is based on the Transformer-based model with state-of-the-art results . the proposed model has little cognitive motivation and is too small to perform ablation studies . |
We Need to Measure Data Diversity in NLP — Better and Broader (2025.emnlp-main)
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| Challenge: | Language models exhibit remarkable natural language understanding and generation capabilities, but they have serious flaws, such as societal biases and spurious correlations. |
| Approach: | They argue that interdisciplinary perspectives are essential for developing more fine-grained and valid measures of data diversity. |
| Outcome: | The proposed measures are based on interdisciplinary perspectives and include a variety of datasets. |
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021.findings-acl)
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| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
NLP Needs Diversity outside of ‘Diversity’ (2025.findings-emnlp)
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| Challenge: | a new position paper argues that diversity in NLP is concentrated on a small number of areas surrounding fairness . |
| Approach: | a new position paper argues that diversity in NLP is disproportionately concentrated on fairness areas. |
| Outcome: | a new position paper argues that diversity in NLP is disproportionately concentrated on fairness areas. |