A Chatbot Framework for the Children's Legal Centre

International audienceThis paper presents a novel method to address legal rights for children through a chatbot framework by integrating machine learning, a dialogue graph, and information extraction. The method addresses a significant problem: we cannot presume that children have common knowledge about their rights or express themselves as an adult might. In our framework, a chatbot user begins a conversation, where based on the circumstance described, a neural network predicts both speech acts, relating to a dialogue graph, and legal types. Based on the legal types, relevant legal rights are returned to the user. Information is extracted throughout the conversation in order to create a case for a legal advisor. In collaboration with the Children's Legal Centre Wales, who advocate for the improvement and dissemination of legal rights in Wales, a corpus has been constructed and a prototype chatbot developed. The framework has been evaluated with classification measures and a user study. The framework can be extended and adapted to other domains

A Chatbot Framework for the Children's Legal Centre

International audienceThis paper presents a novel method to address legal rights for children through a chatbot framework by integrating machine learning, a dialogue graph, and information extraction. The method addresses a significant problem: we cannot presume that children have common knowledge about their rights or express themselves as an adult might. In our framework, a chatbot user begins a conversation, where based on the circumstance described, a neural network predicts both speech acts, relating to a dialogue graph, and legal types. Based on the legal types, relevant legal rights are returned to the user. Information is extracted throughout the conversation in order to create a case for a legal advisor. In collaboration with the Children's Legal Centre Wales, who advocate for the improvement and dissemination of legal rights in Wales, a corpus has been constructed and a prototype chatbot developed. The framework has been evaluated with classification measures and a user study. The framework can be extended and adapted to other domains