Shengyao Zhuang

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PhD student, UQ, Online Learning to Rank, Deep Language Model-based Rankers.

Publications

• Xinyu Mao and Shengyao Zhuang and Bevan Koopman and Guido Zuccon. 2024. Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024).
• Joel Mackenzie and Shengyao Zhuang and Guido Zuccon. 2023. Exploring the Representation Power of SPLADE Models. In The Proceedings of the 2023 ACM SIGIR on International Conference on Theory of Information Retrieval (ICTIR 2023).
• Guido Zuccon and Harry Scells and Shengyao Zhuang. 2023. Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models. In The Proceedings of the 2023 ACM SIGIR on International Conference on Theory of Information Retrieval (ICTIR 2023).
• Shengyao Zhuang and Houxing Ren and Linjun Shou and Jian Pei and Ming Gong and Guido Zuccon and Daxin Jiang. 2023. Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation. In The First Workshop on Generative Information Retrieval.
• Harry Scells and Shengyao Zhuang and Guido Zuccon. 2022. Reduce, Reuse, Recycle: Green Information Retrieval Research. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.
• Hang Li and Ahmed Mourad and Shengyao Zhuang and Bevan Koopman and Guido Zuccon. 2022. Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls. In Accepted by the ACM Transactions on Information Systems (TOIS).
• Hang Li and Shuai Wang and Shengyao Zhuang and Ahmed Mourad and xueguang-ma and jimmy-lin and Guido Zuccon. 2022. To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR' 22).
• Hang Li and Shengyao Zhuang and Ahmed Mourad and Xueguang Ma and Jimmy Lin and Guido Zuccon. 2022. Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study. In Proceedings of the 44th European Conference on Information Retrieval (ECIR 2022).
• Hang Li and Shengyao Zhuang and Xueguang Ma and Jimmy Lin and Guido Zuccon. 2022. Pseudo-Relevance Feedback with Dense Retrievers in Pyserini. In Proceedings of the 26th Australasian Document Computing Symposium (ADCS 2022).
• Shengyao Zhuang and Xinyu Mao and Guido Zuccon. 2022. Robustness of Neural Rankers to Typos: A Comparative Study. In The 26th Australasian Document Computing Symposium (ADCS '22).
• Shengyao Zhuang and Guido Zuccon. 2022. Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training. In The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22).
• Shengyao Zhuang and Zhihao Qiao and Guido Zuccon. 2022. Reinforcement Online Learning to Rank with Unbiased Reward Shaping. In Arxiv preprint.
• Shengyao Zhuang and Hang Li and Guido Zuccon. 2022. Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach. In The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22).
• Shengyao Zhuang and Guido Zuccon. 2022. CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos. In The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22).
• Shuyi Wang and Shengyao Zhuang and Guido Zuccon. 2021. Federated Online Learning to Rank with Evolution Strategies: A Reproducibility Study. In Proceedings of the 43rd European Conference on Information Retrieval (ECIR 2021).
• Shuyi Wang and Bing Liu and Shengyao Zhuang and Guido Zuccon. 2021. Effective and Privacy-preserving Federated Online Learning to Rank. In The Proceedings of the 2021 ACM SIGIR on International Conference on Theory of Information Retrieval (ICTIR 2021).
• Shuai Wang and Shengyao Zhuang and Guido Zuccon. 2021. BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval. In The Proceedings of the 2021 ACM SIGIR on International Conference on Theory of Information Retrieval (ICTIR 2021).
• Shengyao Zhuang and Hang Li and Guido Zuccon. 2021. Deep Query Likelihood Model for Information Retrieval. In 43rd European Conference on IR Research.
• Shengyao Zhuang and Hang Li and Shuai Wang and Guido Zuccon. 2021. IELAB at TREC Deep Learning Track 2021. In TREC 2021 Deep Learning Track.
• Shengyao Zhuang and Guido Zuccon. 2021. How do Online Learning to Rank Methods Adapt to Changes of Intent?. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21).
• Shengyao Zhuang and Guido Zuccon. 2021. Dealing with Typos for BERT-based Passage Retrieval and Ranking. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP'21).
• Shengyao Zhuang and Guido Zuccon. 2021. Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion. In Arxiv preprint.
• Shengyao Zhuang and Guido Zuccon. 2021. TILDE: Term Independent Likelihood moDEl for Passage Re-ranking. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21).
• Sebastian Cross and Hang Li and Shengyao Zhuang and Ahmed Mourad and Bevan Koopman and Guido Zuccon. 2020. UQ IElab at TREC 2020 CAsT Track. In TREC 2020 Decision Track.
• Shengyao Zhuang and Guido Zuccon. 2020. Counterfactual Online Learning to Rank. In 42nd European Conference on IR Research.