Siwei Wu

I am an incoming 2024 Fall PhD in the Manchester NLP group, fortunately, supervised by Prof. Chenghua Lin. Now, I am interning at the Institute for Intelligent Computing (通义千问) of Alibaba.

My research interests lie at AIGC including VLMs, music generation, information retrieval, multimodal retrieval, and RAG I don't want to restrict my research direction and I enjoy doing jobs in meaningful fields.

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• 2024-05-17 – Our paper (SciMMIR) was accepted by ACL'24!

• 2024-02-18 – I intern at the Institute for Intelligent Computing (通义千问) of Alibaba!

• 2023-07-25 – Our paper (Dense-ATOMIC) achieved the ACL'23 Outstanding Paper Award!

• 2023-05-25 – Our paper (Dense-ATOMIC) was nominated for ACL'23 Best Paper!

• 2023-05-04 – 2 papers (Dense-ATOMIC and CPNC) were accepted by ACL'23!

• 2023-4-05 – Created this homepage.

To bridge this information retrieval gap in the scientific domain, this work develops a specialised scientific MMIR (SciMMIR) benchmark by leveraging open-access paper collections to extract data relevant to the scientific domain.

This work mainly solves the bipartite graph properties of commonsense knowledge graph ATOMIC, mines potential multiple paths in ATOMIC, and builds a more complete knowledge graph Dense-ATOMIC.

This work alleviates the problems of edge sparsity and nodes redundancy in the commonsense knowledge graph, and proposes a new commonsense knowledge graph completion framework.

Last modified in Nov. 2022. Design and source code from Jon Barron.