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Ben Kao

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5 papers

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IJCAI Conference 2025 Conference Paper

QBR – A Question-Bank-Based Approach to Fine-Grained Legal Knowledge Retrieval for the General Public

Mingruo Yuan
Ben Kao
Tien-Hsuan Wu

Retrieval of legal knowledge by the general public is a challenging problem due to the technicality of the professional knowledge and the lack of fundamental understanding by laypersons on the subject. Traditional information retrieval techniques assume that users are capable of formulating succinct and precise queries for effective document retrieval. In practice, however, the wide gap between the highly technical contents and untrained users makes legal knowledge retrieval very difficult. We propose a methodology, called QBR, which employs a Questions Bank (QB) as an effective medium for bridging the knowledge gap. We show how the QB is used to derive training samples to enhance the embedding of knowledge units within documents, which leads to effective fine-grained knowledge retrieval. We discuss and evaluate through experiments various advantages of QBR over traditional methods. These include more accurate, efficient, and explainable document retrieval, better comprehension of retrieval results, and highly effective fine-grained knowledge retrieval. We also present some case studies and show that QBR achieves social impact by assisting citizens to resolve everyday legal concerns.

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AILAW Journal 2023 Journal Article

Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model

Mingruo Yuan
Ben Kao
Tien-Hsuan Wu
Michael M. K. Cheung
Henry W. H. Chan
Anne S. Y. Cheung
Felix W. H. Chan
Yongxi Chen

Abstract Access to legal information is fundamental to access to justice. Yet accessibility refers not only to making legal documents available to the public, but also rendering legal information comprehensible to them. A vexing problem in bringing legal information to the public is how to turn formal legal documents such as legislation and judgments, which are often highly technical, to easily navigable and comprehensible knowledge to those without legal education. In this study, we formulate a three-step approach for bringing legal knowledge to laypersons, tackling the issues of navigability and comprehensibility. First, we translate selected sections of the law into snippets (called CLIC-pages), each being a small piece of article that focuses on explaining certain technical legal concept in layperson’s terms. Second, we construct a Legal Question Bank, which is a collection of legal questions whose answers can be found in the CLIC-pages. Third, we design an interactive CLIC Recommender. Given a user’s verbal description of a legal situation that requires a legal solution, CRec interprets the user’s input and shortlists questions from the question bank that are most likely relevant to the given legal situation and recommends their corresponding CLIC pages where relevant legal knowledge can be found. In this paper we focus on the technical aspects of creating an LQB. We show how large-scale pre-trained language models, such as GPT-3, can be used to generate legal questions. We compare machine-generated questions against human-composed questions and find that MGQs are more scalable, cost-effective, and more diversified, while HCQs are more precise. We also show a prototype of CRec and illustrate through an example how our 3-step approach effectively brings relevant legal knowledge to the public.

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AAAI Conference 2023 Conference Paper

CEMA – Cost-Efficient Machine-Assisted Document Annotations

Guowen Yuan
Ben Kao
Tien-Hsuan Wu

We study the problem of semantically annotating textual documents that are complex in the sense that the documents are long, feature rich, and domain specific. Due to their complexity, such annotation tasks require trained human workers, which are very expensive in both time and money. We propose CEMA, a method for deploying machine learning to assist humans in complex document annotation. CEMA estimates the human cost of annotating each document and selects the set of documents to be annotated that strike the best balance between model accuracy and human cost. We conduct experiments on complex annotation tasks in which we compare CEMA against other document selection and annotation strategies. Our results show that CEMA is the most cost-efficient solution for those tasks.

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AAAI Conference 2019 Conference Paper

Spectral Clustering in Heterogeneous Information Networks

Xiang Li
Ben Kao
Zhaochun Ren
Dawei Yin

A heterogeneous information network (HIN) is one whose objects are of different types and links between objects could model different object relations. We study how spectral clustering can be effectively applied to HINs. In particular, we focus on how meta-path relations are used to construct an effective similarity matrix based on which spectral clustering is done. We formulate the similarity matrix construction as an optimization problem and propose the SClump algorithm for solving the problem. We conduct extensive experiments comparing SClump with other state-of-the-art clustering algorithms on HINs. Our results show that SClump outperforms the competitors over a range of datasets w. r. t. different clustering quality measures.

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IJCAI Conference 2016 Conference Paper

Neural Enquirer: Learning to Query Tables in Natural Language

Pengcheng Yin
Zhengdong Lu
Hang Li
Ben Kao

We propose Neural Enquirer - a neural network architecture for answering natural language (NL) questions based on a knowledge base (KB) table. Unlike existing work on end-to-end training of semantic parsers, Neural Enquirer is fully "neuralized": it finds distributed representations of queries and KB tables, and executes queries through a series of neural network components called "executors". Executors model query operations and compute intermediate execution results in the form of table annotations at different levels. Neural Enquirer can be trained with gradient descent, with which the representations of queries and the KB table are jointly optimized with the query execution logic. The training can be done in an end-to-end fashion, and it can also be carried out with stronger guidance, e. g. , step-by-step supervision for complex queries. Neural Enquirer is one step towards building neural network systems that can understand natural language in real-world tasks. As a proof-of-concept, we conduct experiments on a synthetic QA task, and demonstrate that the model can learn to execute reasonably complex NL queries on small-scale KB tables.

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