Di Wu

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I pressed the shutter at random and captured this scene.

Hi there! I’m Di Wu 吴迪, a Phd candidate (starting in Sept 2022) under the supervision of Christof Monz at Language Technology Lab, University of Amsterdam.

I appreciate designing models or approaches that are driven by intuition after capturing deep understandings of specific fields, equipped with modern neural architectures, and grounded in real-world scenarios. The research that attracted me most is simple, insightful, and far-reaching creations or findings, such as Word2Vec.

I mainly focus on Natural Language Processing and more specifically Machine Translation.

Here are some problems I get interested in now. If you’re willing to chat about them, leave me a message. I’m always open to collaborations, or any kind of chat.

Selected Papers

Representational Isomorphism and Alignment of Multilingual Large Language Models
- Di Wu, Yibin Lei, Andrew Yates, Christof Monz, 2024
- EMNLP2024 Findings, [PDF]
How Far can 100 Samples Go? Unlocking Zero-Shot Translation with Tiny Multi-Parallel Data
- Di Wu, Shaomu Tan, Yan Meng, David Stap, Christof Monz, 2024
- ACL2024 Findings, [PDF]
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation
- Di Wu, Christof Monz, 2023
- EMNLP2023, [PDF]
UvA-MT’s Participation in the WMT23 General Translation Shared Task
- Di Wu, Shaomu Tan, David Stap, Ali Araabi, Christof Monz, 2023
- WMT2023, Winning System, [PDF]
SLUA: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning
- Di Wu, Liang Ding, Dacheng Tao, IWSLT 2022
- IWSLT2022, [PDF]
SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling
- Di Wu, Liang Ding, Fan Lu, Jian Xie
- EMNLP2020, [PDF]

Ding~ you may know me better from this personal page.