Multi-Task Reading for Intelligent Legal Services

Course title:Multitasking reading for smart legal services In this context, this article examines the process of legal researchers in empirical legal research, proposes its own solution, and designs multitasking reading for smart legal services. In the editing process, it is necessary to proofread the draft to examine more notable aspects such as organization, paragraph structure, and content. Nevertheless, when proofreading, you need to focus more on finding and improvising errors in writing, grammar, and language. The two main advantages of introducing AI into the legal aspect are: The system first uses statistical analysis methods to fill the structured data measurement portion of empirical research. Such measures can measure structured data in empirical research, such as statistical yearbooks. For unstructured data such as interview recordings and reference documents, the system uses machine-readable comprehension technology to analyze and populate the measurement portion of unstructured data in empirical research. To solve the problem of diversity, this article designs an automatic reading comprehension model capable of multitasking – LegalSelfReader. The model can handle three types of problems: margin extraction, yes or no judgment, and unanswered questions. In principle, this meets the problematic requirements of empirical analysis of the law. Due to structured and unstructured legal data, existing machine-reading comprehension models trained on existing datasets cannot directly build a legal reading understanding system that matches real-world scenarios.

Therefore, this article examines a more reasonable way to build a legal reading comprehension system, i.e. to use an automatic reading comprehension model to process unstructured data such as reference documents and other statistical analysis models, in order to Leading Legal Disruption: Artificial Intelligence and a Toolkit for Lawyers and the Law aims to challenge lawyers to the practical implications of the new technologies on the provision of legal services. and reflection. on legal issues to manage their digital transformation. By inviting thought leaders from around the world and in various disciplines, from privacy, contract law and tort to governance and politics, this book goes beyond abstract and general philosophical observations on issues affecting practitioners. This practical approach has produced a wide range of global perspectives that are refreshing and timely for increasingly global problems. Artificial intelligence (AI) is a term coined by John McCarthy, considered the father of AI. In simpler terms, it can be described as a capable and intelligent machine that can think, understand and act independently, in addition to the ability to replicate certain human behaviors. Therefore, AI is the invention of science and art by programming computers to reflect the thought process, just like humans. One of the main examples is Google Translate, where we can translate any text written in different languages in a timely manner.

Effective evaluation of top-k queries is essential for many applications where a huge amount of data needs to be classified and sorted to give users the best answers in a reasonable amount of time. Examples include e-commerce platforms (e.g., amazon.com), media sharing platforms, web databases, etc. In most cases, these applications need to retrieve data from standalone data sources. These data sources are accessed through common web APIs, such as data web services, to provide a standard way to interact with data. In this context, user queries often require responding to multiple data services. Most existing solutions for evaluating top-k queries assume that data services provide both sorted and random access to data, or simply sorted access. In practice, however, some services may only provide random access to data, which could affect the performance of solutions. In this article, we propose an approach to optimize the evaluation of top-k queries through data services. We look at the worst-case scenario where services provide only random access to data. Our approach defines two strategies: the parallel pipeline strategy and the principle of necessary invocation, in order to reduce the time it takes to process the composition and the number of unnecessary service calls. The experiences conducted have shown the scalability and efficiency of our solution. Legal technology is an area that has essentially become popular in recent years.

It started with simple tasks like document management, court update tracks, and evolved into “mundane” legal tasks and even more accurate research to reduce the scope of human error. One of the difficulties of empirical research is that with unstructured data, such as textual data, researchers can only rely on humans to make measurements. It is a slow and lengthy process. A viable solution is to let the machine-readable comprehension model in natural language processing replace the researcher to read legal judgment documents, the legal record, and other data to fulfill the task of qualitative measurement. The other chapters in this article are organized as follows. Section 2 will present related research on the understanding of machine reading. Section 3 shows the system of legal empirical research designed in this article. Section 4 will demonstrate the performance of the proposed model on a legal reading comprehension dataset. The fifth chapter presents the conclusions of this document and looks forward to it. In this regard, AI-powered legal proofreading tools like Mike DocuSieve are a boon for document proofreading. The speed, accuracy, and quality of review provided by these tools not only help lawyers save time, but also improve the quality of every design they create. Understanding machine reading is an important task in nature Haider Abbas currently heads the R&D department at the Military College of Signals, NUST, Pakistan.

He is Director of the National Cyber Security Auditing and Evaluation Lab (NCSAEL) at MCS NUST. Dr. Abbas is a cybersecurity expert, academic, researcher and industry consultant who has received professional training and certifications from the Massachusetts Institute of Technology (MIT), USA. Stockholm University, Sweden; Stockholm School of Entrepreneurship, Sweden; IBM, USA and Certified Ethical Hacker from the EC-Council. He received his Master`s degree in Engineering and Management of Information Systems (2006) and his PhD in Information Security (2010) from KTHâRoyal Institute of Technology, Stockholm, Sweden. His professional career includes activities ranging from R&D and industrial consultants (governmental and private) to multinational research projects, research grants, doctoral advice, international journal editors, conferences/workshops, guest speaker/keynote speaker, member of the technical program committee and reviewer for several international journals and conferences. In addition, he was appointed by Springer as a full-time regional editor for all submissions from Pakistan and Iran for neural computing and applications (ISI-Indexed, IF 4.6, JCR Ranking Q1). He is an associate professor and doctoral advisor at Al-Farabi Kazakh National University, Almaty, Kazakhstan, Manchester Metropolitan University, United Kingdom, and Florida Institute of Technology, United States. The process of legal empirical analysis is a relatively complex process. It contains many types of data, including structured data such as statistical yearbooks, as well as unstructured data such as interview recordings and reference documents. Therefore, it is a research method that requires both structured and unstructured data analysis.

In general, AI is the duplication of humans and can theoretically perform the same tasks in less time and faster. With the application of AI ranging from ordering groceries on Alexa to creating Spotify playlists based on your music selection, it has also found its place in the legal sector. Since legal data contains both structured and unstructured data, it is very difficult to implement machine reading understanding technology in empirical analysis of law. This article provides multitasking reading for intelligent legal services that applies statistical analysis and machine-reading comprehension techniques and can handle both structured and unstructured data. At the same time, this article proposes a machine reading comprehension model capable of multitasking, LegalSelfReader, which can solve the problem of the diversity of questions. In the CJRC legal reading comprehension dataset experiment, the model proposed in this thesis is far superior to the two classical models of BIDAF and Bert in three evaluation indicators. And our model is also better than some models published by HFL (Harbin Institute of Technology and iFly Joint Lab) and has also reduced the consumption of training costs. At the same time, the experience of visualizing attention value shows that the model proposed in this article has a greater ability to extract evidence. Autoplay comprehension tasks are usually defined as a passage and a pass-related question.