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Creators/Authors contains: "Hoyle, Alexander Miserlis"

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  1. ; ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available April 29, 2026
  2. ; ; ; (, Findings of the Association for Computational Linguistics: EMNLP)
    Goldberg, Yoav; Kozareva, Zornitsa; Zhang, Yue (Ed.)
  3. ; ; (, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))
    null (Ed.)
    Abstract: Topic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers. Our modular method can be straightforwardly applied with any neural topic model to improve topic quality, which we demonstrate using two models having disparate architectures, obtaining state-of-the-art topic coherence. We show that our adaptable framework not only improves performance in the aggregate over all estimated topics, as is commonly reported, but also in head-to-head comparisons of aligned topics. 
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  4. ; ; ; ; ; (, Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers))
    Leaderboards are widely used in NLP and push the field forward. While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models). Rather than replace leaderboards, we advocate a re-imagining so that they better highlight if and where progress is made. Building on educational testing, we create a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses. Using this model, we analyze the ranking reliability of leaderboards. Afterwards, we show the model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples. We conclude with recommendations for future benchmark tasks. 
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