AimeLaw, a research team from Aimesoft Multimodal Artificial Intelligence Lab, won the first rank and the best presentation award at the ALQAC 2021.
We feel highly honored and happy to have won the award. We will expand AimeLaw to support many other languages and to further contribute to the legal text processing research community.”
— Dr. Minh Pham, Director of AIMAI Lab
SAN JOSE, CALIFORNIA, UNITED STATES, November 23, 2021 /EINPresswire.com/ — AimeLaw, a research team from Aimesoft Multimodal Artificial Intelligence Lab (AIMAI Lab), won the first rank and the best presentation award at the Automated Legal Question Answering Competition ALQAC 2021.
ALQAC 2021 is a competition organized by the Japan Advanced Institute of Science and Technology (JAIST) in association with the 13th International Conference on Knowledge and Systems Engineering (KSE 2021), with the aim of promoting research and development of AI software systems that automatically answer legal questions, helping users quickly find answers of legal questions.
In particular, AimeLaw achieved the best performance award (first rank) in the task of Legal Textual Entailment and two runner-up awards in the tasks of Legal Document Retrieval and Legal Question Answering. AimeLaw also won the best presentation award at the competition. In the Legal Textual Entailment task, competitors were required to determine whether a legal statement or query is true or false based on relevant legal articles. Legal Document Retrieval is to retrieve relevant articles to an input legal query. Finally, the Legal Question Answering task is to answer yes/no legal questions and can be solved by combining a legal document retrieval model and a legal textual entailment model. In the competition, by analyzing structural characteristics of legal documents, AimeLaw introduced novel data augmentation and text matching methods to improve the accuracy of the proposed artificial neural network models. The methods proposed by AimeLaw have been shown to be effective in shared tasks at ALQAC 2021.
“We feel highly honored and happy to have won the award. We will expand AimeLaw to support many other languages and to further contribute to the legal text processing research community”, said Dr. Minh Pham, Director of AIMAI Lab and leader of the AimeLaw research team.
AIMAI Lab is a research laboratory inside Aimesoft Inc., which focuses on the research and development of Multimodal AI technologies and applications, such as Natural language processing, Speech processing, Computer Vision, and Data mining technologies. AimeLaw is a research team in AIMAI Lab, which has special research interest in legal text analysis. The technologies that AimeLaw used in the competition have been applied for several real-world legal text analysis software products of Aimesoft.
Previously, Dr. Minh Pham also led other research projects that won many outstanding awards in the field of Natural Language Processing (NLP). Specifically, in the VLSP 2018 and VLSP 2020 workshops, his research group achieved excellent awards on Named Entity Recognition (NER), Social Network Mining and Relation Extraction tasks.
Aimesoft is an AI product and solution company based in San Jose, California. Aimesoft focuses on Multimodal AI, a new AI paradigm that combines multiple input sources (text, voice, image, numerical data) to achieve high performance. The main product of the company is the Multimodal AI software ecosystem Aimenicorn. Aimesoft has deployed more than 100 applications of Multimodal AI to the global market. For more details, please visit https://www.aimesoft.com/
As an associated event of the KSE 2021 conference, ALQAC includes 3 tasks for each language: (1) Legal Document Retrieval, (2) Legal Textual Entailment, and (3) Legal Question Answering. We introduce the Legal Question Answering dataset for the competition – a manually annotated dataset based on well-known statute laws in Vietnamese and Thai Language. Through the competition, they aim to develop a research community on legal support systems. See more details at
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November 23, 2021, 18:00 GMT
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