@inproceedings{d7756cb584bb43e98ba6a876957d7784,
title = "TP-Detector: Detecting Turning Points in the Engineering Process of Large-scale Projects",
abstract = "This paper introduces a novel task of detecting turning points in the engineering process of large-scale projects, wherein the turning points signify significant transitions occurring between phases. Given the complexities involving diverse critical events and limited comprehension in individual news reports, we approach the problem by treating the sequence of related news streams as a window with multiple instances. To capture the evolution of changes effectively, we adopt a deep Multiple Instance Learning (MIL) framework and employ the multiple instance ranking loss to discern the transition patterns exhibited in the turning point window. To facilitate comprehensive evaluation of the task, we curate a dataset comprising 80 large-scale projects. Extensive experiments consistently demonstrate the effectiveness of our proposed approach on the constructed dataset compared to baseline methods. We deployed the proposed model1 and provided a demonstration video2 to illustrate its functionality. The code and dataset are available on GitHub.",
author = "Qi Wu and Wenhan Chao and Xian Zhou and Zhunchen Luo",
note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2023 ; Conference date: 06-12-2023 Through 10-12-2023",
year = "2023",
doi = "10.18653/v1/2023.emnlp-demo.16",
language = "英语",
series = "EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings of the System Demonstrations",
publisher = "Association for Computational Linguistics (ACL)",
pages = "177--185",
editor = "Yansong Feng and Els Lefever",
booktitle = "EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings of the System Demonstrations",
address = "澳大利亚",
}