Transformers for Multi-Intent Classification and Slot Filling of Supreme Court Decisions Related to Sexual Violence Law

被引:1
|
作者
Munthuli, Adirek [1 ,2 ]
Socatiyanurak, Vorada [1 ]
Sangchocanonta, Sirikorn [1 ,2 ]
Kovudhikulrungsri, Lalin [3 ]
Saksakulkunakorn, Nantawat [3 ]
Chairuangsri, Phornkanok [3 ]
Tantibundhit, Charturong [1 ,2 ]
机构
[1] Thammasat Univ, Ctr Excellence Intelligent Informat Speech & Langu, Pathum Thani 12120, Thailand
[2] Thammasat Univ, Fac Engn, Thammasat Sch Engn, Dept Elect & Comp Engn, Rangsit Campus, Pathum Thani 12120, Thailand
[3] Thammasat Univ, Fac Law, Rangsit Campus, Pathum Thani 12120, Thailand
关键词
Intent classification; legal informatics; sexual violence; Thai law; transformers; VICTIMS; ASSAULT;
D O I
10.1109/ACCESS.2023.3296261
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Sexual violence is a pervasive and complex issue that demands an immediate and comprehensive solution. The previous study titled "LAW-U: Legal Guidance Through Artificial Intelligence Chatbot for Sexual Violence Victims and Survivors" highlighted the crucial role of technology in addressing this problem. The current study aims to overcome limitations in the previous study by investigating the use of transformer-based models for multi-intent classification of Thai Supreme Court decision fragments related to Section 276 of the Thai Criminal Code. Utilizing various evaluation matrices, the study evaluates the effectiveness of transfer learning through transformer-based pre-trained language models against a Word2Vec-based support vector machine to detect criminal intents from decision fragments. The results demonstrate that transformer-based models, particularly XLM-RoBERTa(BASE), outperform the Word2Vec-based support vector machine in multi-intent classification. The macro average F1-score of 0.77 and micro average F1-score of 0.78 achieved by the best-performing model indicates the effectiveness of pre-trained transformers with fine-tuning. The study also employs a t-SNE visualization to gain insights into the overlapping between criminal intents and the areas where misclassifications occur. The visualization reveals that misclassification occur between closely related or overlapping intents, especially when decision fragments have multiple intents. Overall, the study contributes to the field of legal technology by creating a model that can accurately classify criminal intents related to Section 276, which can be extrapolated to other sexual violence laws. The model will be used to train the updated version of LAW-U, specifically called LAW-U-RoBERTa, which will provide legal recommendations to sexual violence survivors and empower them to reaffirm their inherent rights through seeking justice. The study demonstrates the potential of artificial intelligence chatbots to support survivors of sexual violence and contribute to the fight against the pervasive problem of sexual violence.
引用
收藏
页码:76448 / 76467
页数:20
相关论文
共 9 条
  • [1] SLIM: EXPLICIT SLOT-INTENT MAPPING WITH BERT FOR JOINT MULTI-INTENT DETECTION AND SLOT FILLING
    Cai, Fengyu
    Zhou, Wanhao
    Mi, Fei
    Faltings, Boi
    2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, : 7607 - 7611
  • [2] Promoting Unified Generative Framework with Descriptive Prompts for Joint Multi-Intent Detection and Slot Filling
    Ma, Zhiyuan
    Qin, Jiwei
    Pan, Meiqi
    Tang, Song
    Mi, Jinpeng
    Liu, Dan
    ELECTRONICS, 2024, 13 (06)
  • [3] PAPER Special Technology Support Hyperconnectivity Conceptual Knowledge Enhanced Model for Multi-Intent Detection and Slot Filling
    He, Li
    Zhao, Jingxuan
    Duan, Jianyong
    Wang, Hao
    Li, Xin
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2024, E107D (04) : 468 - 476
  • [4] A Deep Multi-task Model for Dialogue Act Classification, Intent Detection and Slot Filling
    Mauajama Firdaus
    Hitesh Golchha
    Asif Ekbal
    Pushpak Bhattacharyya
    Cognitive Computation, 2021, 13 : 626 - 645
  • [5] A Deep Multi-task Model for Dialogue Act Classification, Intent Detection and Slot Filling
    Firdaus, Mauajama
    Golchha, Hitesh
    Ekbal, Asif
    Bhattacharyya, Pushpak
    COGNITIVE COMPUTATION, 2021, 13 (03) : 626 - 645
  • [6] A Transformer based Multi-task Model for Domain Classification, Intent Detection and Slot-Filling
    Saha, Tulika
    Priya, Neeti
    Saha, Sriparna
    Bhattacharyya, Pushpak
    2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,
  • [7] Co-RGCN: A Bi-path GCN-Based Co-Regression Model for Multi-intent Detection and Slot Filling
    Wen, Qingpeng
    Zeng, Bi
    Wei, Pengfei
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2023, PT IV, 2023, 14257 : 316 - 327
  • [8] Ius Constitutionale Commune: the potential expansion of the sexual minorities' protection in the Federal Supreme Court's case law through the recognition of the "suspect classification" status and the incorporation of the Inter-american Court of Human Rights' precedents
    Leal, Monia Clarissa Hennig
    de Vargas, Eliziane Fardin
    DIREITO E PRAXIS, 2022, 13 (02): : 1319 - 1354
  • [9] Applying International Experiences in National Prosecutions of Conflict-related Sexual Violence A Case Study of Application of the ICTY Law, Findings and Practices in Prosecutions before the Court of Bosnia and Herzegovina
    Ferizovic, Jasenka
    Mlinarevic, Gorana
    JOURNAL OF INTERNATIONAL CRIMINAL JUSTICE, 2020, 18 (02) : 325 - 348