Papers by Ji-Ung Lee
Efficient Methods for Natural Language Processing: A Survey (2023.tacl-1)
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Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon Derczynski, Iryna Gurevych, Roy Schwartz
| Challenge: | Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data, but using only scale to improve performance means resource consumption also grows. |
| Approach: | They propose to use data, time, storage, or energy to improve model performance. |
| Outcome: | The proposed methods and findings provide guidance for conducting NLP under limited resources and point towards promising research directions for developing more efficient methods. |
Transformers with Learnable Activation Functions (2023.findings-eacl)
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| Challenge: | Activation functions can reduce the topological complexity of input data and improve model performance. |
| Approach: | They propose to consider data as a topology with its own shape to simplify its complexity and make it linearly separable in the output space. |
| Outcome: | The RAF-based Transformer model outperforms its FAF-based counterpart on the GLUE benchmark by 5.71 points and 2.05 points on SQuAD with all available data. |
Manipulating the Difficulty of C-Tests (P19-1)
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| Challenge: | a new manipulation strategy for increasing and decreasing difficulty of C-tests is proposed for language learning . we use two strategies to generate C- tests with the desired difficulty level . learning languages is of utmost importance in an international society . |
| Approach: | They propose two manipulation strategies for increasing and decreasing difficulty of C-tests automatically. |
| Outcome: | The proposed manipulation strategies increase and decrease difficulty of C-tests automatically. |
Investigating label suggestions for opinion mining in German Covid-19 social media (2021.acl-long)
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| Challenge: | Existing difficulties in data annotation are due to prolonged data gathering processes or opinion surveys being subject to reactivity. |
| Approach: | They propose to use label suggestions to improve annotation efficiency in german Covid-19 data by providing annotators with pre-recorded annotations. |
| Outcome: | The proposed model improves inter-annotator agreement and annotation quality in a controlled study with social science students. |
TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation (2022.aacl-demo)
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Lorenz Stangier, Ji-Ung Lee, Yuxi Wang, Marvin Müller, Nicholas Frick, Joachim Metternich, Iryna Gurevych
| Challenge: | TexPrax is a messaging system to collect and annotate task-oriented dialog data . informal communication channels such as instant messengers are increasingly being used at work . |
| Approach: | They propose a messaging system that collects and annotates task-oriented dialog data from employees via chatbots. |
| Outcome: | The proposed system collects and annotates tasks-oriented dialog data from german factory workers and provides lightweight annotations. |
Empowering Active Learning to Jointly Optimize System and User Demands (2020.acl-main)
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| Challenge: | Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. |
| Approach: | They propose an active learning approach that integrates active learning with an end-user application to optimize the user's training and receiving useful instances. |
| Outcome: | The proposed approach satisfies both objectives when alternative methods lead to many unsuitable exercises for end users. |
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems (N19-1)
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Steffen Eger, Gözde Gül Şahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych
| Challenge: | Recent studies show that visual similarity can play a decisive role in assessing the meaning of characters. |
| Approach: | They investigate the impact of visual adversarial attacks on current NLP systems . they explore three shielding methods that significantly improve the robustness of the models . |
| Outcome: | The proposed methods improve performance but still fall behind non-attack scenarios. |
Lessons Learned from a Citizen Science Project for Natural Language Processing (2023.eacl-main)
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Jan-Christoph Klie, Ji-Ung Lee, Kevin Stowe, Gözde Şahin, Nafise Sadat Moosavi, Luke Bates, Dominic Petrak, Richard Eckart De Castilho, Iryna Gurevych
| Challenge: | Annotations are expensive and difficult to obtain, which is why many NLP systems outsource their work to paid crowdworkers. |
| Approach: | They propose to use Citizen Science to re-annotate parts of a pre-existing crowdsourced dataset to gain high-quality annotations. |
| Outcome: | The proposed approach yields high-quality annotations and motivated volunteers, but requires consideration of scalability, participation over time, and legal and ethical issues. |