What’s Wrong, Python? – A Visual Differ and Graph Library for NLP in Python (L18-1)
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| Challenge: | a library that allows the user to visualise and compare the output of a program with a well-known data format is needed. |
| Approach: | They propose a supervised learning tool that allows users to visualise and compare program output . they use popular off-the-shelf visualisation programs to specify essential primitive functions . |
| Outcome: | The proposed tool gives the user total control over visualisation and compares output of any program with a well-known data format. |
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Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth
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NLP+Vis: NLP Meets Visualization (2023.emnlp-tutorial)
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| Challenge: | This tutorial will introduce NLP+Vis with a focus on two main threads of work: NLP for Vis and Vis for NLP. |
| Approach: | tutorial will introduce NLP+Vis with a focus on two main threads of work . overview of research topics on combining NLP and Vis techniques will be covered . |
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On Measures of Biases and Harms in NLP (2022.findings-aacl)
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Sunipa Dev, Emily Sheng, Jieyu Zhao, Aubrie Amstutz, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Akihiro Nishi, Nanyun Peng, Kai-Wei Chang
| Challenge: | Recent studies show that natural language processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality. |
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Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)
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| Challenge: | Current NLP models heavily rely on effective representation learning algorithms. |
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Do Text-to-Vis Benchmarks Test Real Use of Visualisations? (2024.emnlp-main)
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| Challenge: | Existing benchmarks for visualisations are limited and do not reflect real-world use. |
| Approach: | They analysed visualisation code from Python, R, Javascript, and Vega to find similarities and differences between real-world and benchmark datasets. |
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Deep Learning on Graphs for Natural Language Processing (2021.naacl-tutorials)
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| Challenge: | Graph Neural Networks (GNNs) are powerful tools for non-Euclidean data modeling and are used in many graph-related NLP tasks. |
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NLP Scholar: An Interactive Visual Explorer for Natural Language Processing Literature (2020.acl-demos)
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Reviewing Natural Language Processing Research (2021.eacl-tutorials)
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| Challenge: | a tutorial on reviewing is a useful tool for researchers who are new to the field of NLP. |
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A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
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| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
| Approach: | This tutorial provides an up-to-date guide to the recent datasets . it surveys old and new methodological issues with dataset construction . |
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A Survey of Cognitive Distortion Detection and Classification in NLP (2025.findings-emnlp)
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| Challenge: | despite momentum in natural language processing, the field remains fragmented . inconsistencies in CD taxonomies, task formulations and evaluation practices limit comparability . |
| Approach: | This review provides a comprehensive review of 38 studies spanning two decades . they map how CDs have been implemented in computational research and evaluate the methods applied. |
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