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Creators/Authors contains: "Zhu, Yaxin"

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  1. ; (, Findings of the Association for Computational Linguistics: Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024))
    This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space. While existing research has primarily focused on fully supervised XMC, real-world scenarios often lack supervision signals, highlighting the im- portance of zero-shot settings. Given the large label space, utilizing in-context learning approaches is not trivial. We address this issue by introducing In-Context Extreme Multi-label Learning (ICXML), a two-stage framework that cuts down the search space by generating a set of candidate labels through in-context learning and then reranks them. Extensive experiments suggest that ICXML advances the state of the art on two diverse public benchmarks. 
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    Accepted Manuscript Full Text Available
  2. ; (, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022))
    Concept prerequisite learning (CPL) plays a key role in developing technologies that assist people to learn a new complex topic or concept. Previous work commonly assumes that all concepts are given at training time and solely focuses on predicting the unseen prerequisite relationships between them. However, many real-world scenarios deal with concepts that are left undiscovered at training time, which is relatively unexplored. This paper studies this problem and proposes a novel alternating knowledge distillation approach to take advantage of both content- and graph-based models for this task. Extensive experiments on three public benchmarks demonstrate up to 10% improvements in terms of F1 score. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; (, SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval)
    null (Ed.)
    Full Text Available 
  4. ; ; ; ; (, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies)
    null (Ed.)
    Full Text Available