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Creators/Authors contains: "Wang, Zhengyang"

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  1. ; ; ; ; ; (, The Second Conference on Parsimony and Learning)
    Free, publicly-accessible full text available February 11, 2026
  2. ; ; ; ; ; ; ; ; ; (, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; ; ; (, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing)
    Full Text Available 
  4. ; ; ; ; ; ; ; ; ; ; et al (, KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining)
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ; ; ; (, Proceedings of IEEE International Conference on Data Engineering (ICDE))
    Full Text Available 
  6. ; ; ; ; ; ; ; ; ; ; et al (, ICLR)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ; ; (, Association for Computational Linguistics)
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  8. ; ; ; ; ; ; ; (, In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023))
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  9. ; ; ; ; ; ; ; (, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics)
    Product catalogs, conceptually in the form of text-rich tables, are self-reported by individual retailers and thus inevitably contain noisy facts. Verifying such textual attributes in product catalogs is essential to improve their reliability. However, popular methods for processing free-text content, such as pre-trained language models, are not particularly effective on structured tabular data since they are typically trained on free-form natural language texts. In this paper, we present Tab-Cleaner, a model designed to handle error detection over text-rich tabular data following a pre-training / fine-tuning paradigm. We train Tab-Cleaner on a real-world Amazon Product Catalog table w.r.t millions of products and show improvements over state-of-the-art methods by 16% on PR AUC over attribute applicability classification task and by 11% on PR AUC over attribute value validation task. 
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  10. ; ; ; ; ; ; ; ; ; ; et al (, KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining)
    Accepted Manuscript Full Text Available