FALQU: Finding Answers to Legal Questions

TL;DR

FALQU is a new legal question-answering dataset derived from Law Stack Exchange, featuring diverse real-world legal questions and answers, and includes baseline retrieval system evaluations.

Contribution

This paper introduces FALQU, the first large-scale legal IR test collection from LawSE, with diverse questions and baseline system evaluations.

Findings

BM25 achieved the highest effectiveness among tested models.

Traditional IR models perform competitively on legal questions.

The dataset captures real-world legal information needs.

Abstract

This paper presents a new test collection for Legal IR, FALQU: Finding Answers to Legal Questions, where questions and answers were obtained from Law Stack Exchange (LawSE), a Q&A website for legal professionals, and others with experience in law. Much in line with Stack overflow, Law Stack Exchange has a variety of questions on different topics such as copyright, intellectual property, and criminal laws, making it an interesting source for dataset construction. Questions are also not limited to one country. Often, users of different nationalities may ask questions about laws in different countries and expertise. Therefore, questions in FALQU represent real-world users' information needs thus helping to avoid lab-generated questions. Answers on the other side are given by experts in the field. FALQU is the first test collection, to the best of our knowledge, to use LawSE, considering more diverse questions than the questions from the standard legal bar and judicial exams. It contains 9880 questions and 34,145 answers to legal questions. Alongside our new test collection, we provide different baseline systems that include traditional information retrieval models such as TF-IDF and BM25, and deep neural network search models. The results obtained from the BM25 model achieved the highest effectiveness.