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20122024

Research activity per year

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  • HateModerate: Testing Hate Speech Detectors against Content Moderation Policies

    Zheng, J., Liu, X., Yang, G., Haque, M., Qian, X., Rathnasuriya, R., Yang, W. & Budhrani, G., 2024, Findings of the Association for Computational Linguistics: NAACL 2024 - Findings. Duh, K., Gomez, H. & Bethard, S. (eds.). p. 2691-2710 20 p. (Findings of the Association for Computational Linguistics: NAACL 2024 - Findings).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Scopus citations
  • Automated Machine Learning & Tuning with FLAML

    Wang, C., Wu, Q., Liu, X. & Quintanilla, L., 14 Aug 2022, KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. p. 4828-4829 2 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Scopus citations
  • TestAug: A Framework for Augmenting Capability-based NLP Tests

    Yang, G., Haque, M., Song, Q., Yang, W. & Liu, X., 2022, In: Proceedings - International Conference on Computational Linguistics, COLING. 29, 1, p. 3480-3495 16 p.

    Research output: Contribution to journalConference articlepeer-review

    6 Scopus citations
  • An empirical study on hyperparameter optimization for fine-tuning pre-trained language models

    Liu, X. & Wang, C., 2021, ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference. p. 2286-2300 15 p. (ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    9 Scopus citations
  • Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-trained Language Models

    Yang, G., Dineen, S., Lin, Z. & Liu, X., 2021, Deployable Machine Learning for Security Defense - 2nd International Workshop, MLHat 2021, Proceedings. Wang, G., Ciptadi, A. & Ahmadzadeh, A. (eds.). p. 55-78 24 p. (Communications in Computer and Information Science; vol. 1482 CCIS).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    7 Scopus citations