A Statutory Article Retrieval Dataset in F rench (2022)

Abstract: Statutory article retrieval is the task of automatically retrieving law articles relevant to a legal question. While recent advances in natural language processing have sparked considerable interest in many legal tasks, statutory article retrieval remains primarily untouched due to the scarcity of large-scale and high-quality annotated datasets. To address this bottleneck, we introduce the Belgian Statutory Article Retrieval Dataset (BSARD), which consists of 1,100+ French native legal questions labeled by experienced jurists with relevant articles from a corpus of 22,600+ Belgian law articles. Using BSARD, we benchmark several state-of-the-art retrieval approaches, including lexical and dense architectures, both in zero-shot and supervised setups. We find that fine-tuned dense retrieval models significantly outperform other systems. Our best performing baseline achieves 74.8% R@100, which is promising for the feasibility of the task and indicates there is still room for improvement. By the specificity of the domain and addressed task, BSARD presents a unique challenge problem for future research on legal information retrieval. Our dataset and source code are publicly available.

## introduction legal issues are an integral part of many people's lives @xcite . however, the majority of citizens have little to no knowledge about their rights and fundamental legal processes @xcite . as the internet has become the primary source of information in response to life problems @xcite , people increasingly turn to search engines when faced with a legal issue @xcite . nevertheless, the quality of the search engine's legal help results is currently unsatisfactory, as top results mainly refer people to commercial websites that provide basic information as a way to advertise for-profit services @xcite . on average, only one in five persons obtain help from the internet to clarify or solve their legal issue @xcite . as a result, many vulnerable citizens who cannot afford a legal expert's costly assistance are left unprotected or even exploited. this barrier to accessing legal information creates a clear imbalance within the legal system, preventing the right to equal access to justice for all. people do not need legal services in and of themselves; they need the ends that legal services can provide. recent advances in natural language processing (nlp), combined with the increasing amount of digitized textual data in the legal domain, offer new possibilities to bridge the gap between people and the law. for example, legal judgment prediction @xcite @xcite @xcite may assist citizens in finding insightful patterns between their case and its outcome. additionally, legal text summarization @xcite and automated contract review @xcite may help people clarify long, complex, and ambiguous legal documents. in this work, we focus on statutory article retrieval, which, given a legal question such as "is it legal to contract a lifetime lease? ", aims to return one or several relevant law articles from a body of legal statutes @xcite , as illustrated in figure 1 . a qualified statutory article retrieval system could provide a professional assisting service for unskilled humans and help empower the weaker parties when used for the public interest. finding relevant statutes to a legal question is a challenging task. unlike traditional ad-hoc information retrieval @xcite , statutory article retrieval deals with two types of language: common natural language for the questions and complex legal language for the statutes. this difference in language distribution greatly complicates "should physicians, surgeons, health officers, pharmacists, midwives, and all others who, through their status or profession, be in possession of information confided to them reveal such secrets, they shall be punished with imprisonment of one to three years and a fine of 100 to 1000 euros or one of these penalties only -unless called to testify as a witness in a court of law (or before a parliamentary commission of inquiry) or compelled by a decree or order to divulge the secret." -article 458, penal code the retrieval task as it indirectly requires an inherent interpretation system that can translate a natural question from a non-expert to a legal question to be matched against statutes. for skilled legal experts, these interpretations come from their knowledge of a question's domain and their understanding of the legal concepts and processes involved. nevertheless, an interpretation is rarely unique. instead, it is the interpreter's subjective belief that gives meaning to the question and, accordingly, an idea of the domains in which the answer can be found. as a result, the same question can yield different paths to the desired outcome depending on its interpretation, making statutory article retrieval a difficult and time-consuming task. besides, statutory law is not a stack of independent articles to be treated as complete sources of information on their own -unlike news or recipes. instead, it is a structured and hierarchical collection of legal provisions that have whole meaning only when considered in their overall context, i.e., together with the supplementary information from their neighboring articles, the fields and sub-fields they belong to, and their place in the hierarchy of the law. for instance, the answer to the question "can i terminate an employment contract? " will most often be found in labor law. however, this is not necessarily true if an employer is contracting a self-employed worker to carry out a specific task, in which case the answer probably lies at the higher level of contract law. this example illustrates the importance of considering the question's context and understanding the hierarchical structure of the law when looking for relevant statutory articles. in order to study whether retrieval models can approximate the efficiency and reliability of legal experts, we need a suitable labeled dataset. however, such datasets are difficult to obtain considering that, although statutory provisions are generally publicly accessible (yet often not in a machine-readable format), the questions posed by citizens are not. this work presents a novel french native expertannotated statutory article retrieval dataset as its main contribution. our belgian statutory article retrieval dataset (bsard) consists of more than 1,100 legal questions posed by belgian citizens and labeled by legal experts with references to relevant articles from a corpus of around 22,600 belgian law articles. as a second contribution, we establish strong baselines on bsard by comparing diverse state-of-the-art retrieval approaches from lexical and dense architectures. our results show that fine-tuned dense retrieval models significantly outperform other approaches yet suggest ample opportunity for improvement. we publicly release our dataset and source code at https: //github.com/maastrichtlawtech/bsard. ## related work due to the increasing digitization of textual legal data, the nlp community has recently introduced more and more datasets to help researchers build reliable models on several legal tasks. for instance, @xcite introduced a legal question answering (lqa) dataset with 400 multi-choices questions based on the us national bar exam. similarly, zhong et al. (2020) released an lqa dataset based on the chinese bar exam consisting of 26,365 multiple-choice questions, together with a database of evidence that includes 3,382 chinese legal provisions and the content of the national examination counseling book. furthermore, duan et al. (2019) proposed a legal reading comprehension dataset with 52,000 question-answer pairs crafted on the fact descriptions of 10,000 cases from the supreme people's court of china. on a different note, xiao et al. (2018) presented a dataset for legal judgment prediction (ljp) with around 2.68 million chinese criminal cases annotated with 183 law articles and 202 charges. likewise, chalkidis et al. (2019a) introduced an ljp dataset consisting of 11,478 english cases from the european court of human rights labeled with the associated final decision. meanwhile, xiao et al. (2019) introduced a dataset for similar case matching with 8,964 triplets of cases published by the supreme people's court of china, and chalkidis et al. (2019b) released a text classification dataset containing 57,000 english eu legislative documents tagged with 4,271 labels from the european vocabulary. additionally, manor and li (2019) introduced a legal text summarization dataset consisting of 446 sets of contract sections and corresponding reference summaries, and holzenberger et al. (2020) presented a statutory reasoning dataset based on us tax law. recently, @xcite proposed a dataset for legal contract review that includes 510 contracts annotated with 41 different clauses for a total of 13,101 annotations. in the same vein, @xcite introduced a semantic retrieval dataset for contract discovery with more than 2,500 annotations in around 600 documents. lastly, the coliee case law corpus @xcite ) is a case law retrieval and entailment dataset that includes 650 base cases from the federal court of canada, each with 200 candidate cases to be identified as relevant to the base case. regarding statutory article retrieval, the only other publicly available dataset is the coliee statute law corpus @xcite . it comprises 696 questions from the japanese legal bar exam labeled with references to relevant articles from the japanese civil code, where both the questions and articles have been translated from japanese to english. however, this dataset focuses on legal bar exam question answering, which is quite different from legal questions posed by ordinary citizens. while the latter tend to be vague and straightforward, bar exam questions are meant for aspiring lawyers and are thus specific and advanced. besides, the dataset only contains closed questions (i.e., questions with "yes" or "no" answers) and considers almost 30 times fewer law articles than bsard does. also, unlike bsard, the data are not native sentences but instead translated from a foreign language with a completely different legal system. 1 as a result, the translated dataset may not accurately reflect the logic of the original legal system and language. these limitations suggest the need for a novel large-scale citizen-centric native dataset for statutory article retrieval, which is the core contribution of the present work. ## models formally speaking, a statutory article retrieval system r : (q, c) → f is a function that takes as input a question q along with a corpus of law articles c, and returns a much smaller filter set f ⊂ c of the supposedly relevant articles, ranked by decreasing order of relevance. for a fixed @xmath0, the retriever can be evaluated in isolation with multiple rank-based metrics (see section 5.1). the following section describes the retrieval models we use as a benchmark for the task. ## experiments we now describe the setup we use for experiments and evaluate the performance of our models. ## discussion this section discusses the limitations and broader impacts of our dataset. ## conclusion in this paper, we present the belgian statutory article retrieval dataset (bsard), a citizen-centric french native dataset for statutory article retrieval. within a larger effort to bridge the gap between people and the law, bsard provides a means of evaluating and developing models capable of retrieving law articles relevant to a legal question posed by a layperson. we benchmark several strong information retrieval baselines that show promise for the feasibility of the task yet indicate room for improvement. in the future, we plan to build retrieval models that can handle lengthy statutory articles and inherently exploit the hierarchy of the law. in closing, we hope that our work sparks interest in developing practical and reliable statutory article retrieval models to help improve access to justice for all. steve yuan, chris tar, yun-hsuan sung, brian strope, and ray kurzweil. 2020. multilingual universal sentence encoder for semantic retrieval. in proceedings of the 58th annual meeting of the association for computational linguistics: system demonstrations, acl 2020, pages 87-94. association for computational linguistics. haoxi zhong, zhipeng guo, cunchao tu, chaojun xiao, zhiyuan liu, and maosong sun. 2018. legal judgment prediction via topological learning. in proceedings of the 2018 conference on empirical methods in natural language processing, pages 3540-3549. association for computational linguistics. haoxi zhong, chaojun xiao, cunchao tu, tianyang zhang, zhiyuan liu, and maosong sun. 2020. jec-qa: a legal-domain question answering dataset. in the thirty-fourth aaai conference on artificial intelligence, volume 34(05), pages 9701-9708. aaai press.