Papers by Zi Long Zhu
Extracting, Detecting, and Generating Research Questions for Scientific Articles (2025.coling-main)
Copied to clipboard
Sina Taslimi, Artemis Capari, Hosein Azarbonyad, Zi Long Zhu, Zubair Afzal, Evangelos Kanoulas, George Tsatsaronis
| Challenge: | Existing tools to generate and extract RQs from scientific articles lack a definition of RQ in articles. |
| Approach: | They propose to use a set of regular expressions to identify articles with well-defined RQs and a detection component to identify more complex RQ's in articles. |
| Outcome: | The proposed pipeline can detect and generate RQs from scientific articles and generate high-quality ones. |
Scalable Patent Classification with Aggregated Multi-View Ranking (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing classification-based models struggle with scaling to large numbers of labels and generalizing to new labels. |
| Approach: | They propose a ranking-based method that integrates different views of patents and a meta-model that aggregates and ranks the labels given by the ranking models. |
| Outcome: | The proposed method outperforms the current state-of-the-art methods on two datasets . it can alleviate the limitations and achieve a significant performance improvement . |
Enhancing Extreme Multi-Label Text Classification: Addressing Challenges in Model, Data, and Evaluation (2023.emnlp-industry)
Copied to clipboard
Dan Li, Zi Long Zhu, Janneke van de Loo, Agnes Masip Gomez, Vikrant Yadav, Georgios Tsatsaronis, Zubair Afzal
| Challenge: | Existing approaches to extreme multi-label text classification face inherent challenges in terms of model, data, and evaluation. |
| Approach: | They propose a label ranking model as an alternative to the conventional SciBERT-based classification model and an active learning-based pipeline that addresses the data scarcity of new labels during the update of a classification system. |
| Outcome: | The proposed model enables efficient handling of large-scale labels and accommodates new labels. |
Few-shot initializing of Active Learner via Meta-Learning (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in few-shot and zero-shot learning have limited performance in domain specific applications. |
| Approach: | They propose to initialize an active learner with meta-learned parameters and generate task dependent softmax weights for active learning. |
| Outcome: | The proposed method performs better than the baseline at low budget, the authors show . they show that adding meta-learned learning rates and generating the softmax have negative consequences . |