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Measuring Popularity of Machine-Generated Sentences Using Term Count, Document Frequency, and Dependency Language Model.

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Deep Model Compression Also Helps Models Capture Ambiguity., and . ACL (1), page 6893-6905. Association for Computational Linguistics, (2023)The First Visual Object Tracking Segmentation VOTS2023 Challenge Results., , , , , , , , , and 119 other author(s). ICCV (Workshops), page 1788-1810. IEEE, (2023)Measuring Popularity of Machine-Generated Sentences Using Term Count, Document Frequency, and Dependency Language Model., , , , , and . PACLIC, ACL, (2015)Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for Tigrinya., , , and . ACL (1), page 11857-11870. Association for Computational Linguistics, (2023)Sentential Paraphrase Generation for Agglutinative Languages Using SVM with a String Kernel., , , , and . PACLIC, page 650-657. The PACLIC 28 Organizing Committee and PACLIC Steering Committee / ACL / Department of Linguistics, Faculty of Arts, Chulalongkorn University, (2014)The correlation between search quality and query popularity., , , and . BigComp, page 353-356. IEEE Computer Society, (2016)Self-Knowledge Distillation for Learning Ambiguity., , , and . CoRR, (2024)Addressing the Occlusion Problem in Multi-Camera People Tracking with Human Pose Estimation., , , and . CVPR Workshops, page 5463-5469. IEEE, (2023)Constructing a paraphrase database for agglutinative languages., , , and . Data Knowl. Eng., (2019)Initiating Moderation in Problematic Smartphone Usage Patterns., and . CHI Extended Abstracts, page 1585-1590. ACM, (2015)