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Common Flaws in NLP Evaluation Experiments

Ehud Reiter

The ReproHum project (where I am working with Anya Belz (PI) and Craig Thomson (RF) as well as many partner labs) is looking at the reproducibility of human evaluations in NLP. So User interface problems : Very few NLP papers give enough information about UIs to enable reviewers to check these for problems. Especially

NLP 259
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ACL 2022 Highlights

Sebastian Ruder

ACL 2022 took place in Dublin from 22nd–27th May 2022. Language diversity and multimodality Panelists and their spoken languages at the ACL 2022 keynote panel on supporting linguistic diversity. My invited talk on scaling NLP systems to the next 1000 languages. The KinyaBERT model architecture.

NLP 52
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2022: We reviewed this year’s AI breakthroughs

Applied Data Science

Just wait until you hear what happened in 2022. In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. In 2022 we got diffusion models ( NeurIPS paper ).

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The Geographic Diversity of NLP Conferences

Marek Rei

The growth of interest in NLP technology, fuelled largely by investment in AI applications, has been accompanied by unprecedented expansion of the preeminent NLP conferences: ACL, NAACL and EMNLP in particular. Paper count by country at the 2018 NLP conferences. Normalized paper count by country at the 2018 NLP conferences.

NLP 52
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SQuARE: Towards Multi-Domain and Few-Shot Collaborating Question Answering Agents

ODSC - Open Data Science

QA is a critical area of research in NLP, with numerous applications such as virtual assistants, chatbots, customer support, and educational platforms. Iryna is co-director of the NLP program within ELLIS, a European network of excellence in machine learning. SQuARE is a research project that aims to make QA research more accessible.

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Supporting Human-AI Collaboration in Auditing LLMs with LLMs

ML @ CMU

Trends Human Computer Interaction. [2] Adaptive Testing and Debugging of NLP Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). [3] In CHI Conference on Human Factors in Computing Systems. [5] 2] Marco Tulio Ribeiro and Scott Lundberg.

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AI2 at ACL 2023

Allen AI

NLPositionality: Characterizing Design Biases of Datasets and Models Sebastin Santy, Jenny Liang, Ronan Le Bras*, Katharina Reinecke, Maarten Sap* Design biases in NLP systems, such as performance differences for different populations, often stem from their creator’s positionality, i.e., views and lived experiences shaped by identity and background.