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| "paper_id": "2021", |
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| "date_generated": "2023-01-19T14:46:57.665140Z" |
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| "title": "LexGLUE: A Benchmark Dataset for Legal Language Understanding in English", |
| "authors": [ |
| { |
| "first": "Nikolaos", |
| "middle": [], |
| "last": "Aletras", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "Tomaso", |
| "middle": [], |
| "last": "Agnoloni", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "Andrew", |
| "middle": [], |
| "last": "Blair-Stanek", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "Jessy", |
| "middle": [], |
| "last": "Junyi", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Li", |
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| "abstract": "Law, interpretations of law, legal arguments, agreements, etc. are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.", |
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| "abstract": [ |
| { |
| "text": "Law, interpretations of law, legal arguments, agreements, etc. are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.", |
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| "section": "Abstract", |
| "sec_num": null |
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| "body_text": [ |
| { |
| "text": "Welcome to the third edition of the NLLP (Natural Legal Language Processing) Workshop, co-located with the 2021 Conference on Empirical Methods in Natural Language Processing.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "Different industrial sectors have embraced natural language processing (NLP) technologies, which have altered services and products in healthcare, finance, education among others. The legal domain provides enormous potential for generating interesting research problems. Electronic tools are increasingly used for all types of legal tasks and that use is predicted to grow sharply. By its very nature, the practice of law necessarily involves the analysis and interpretation of language. The potential for NLP applications to provide benefit to practitioners of law and consumers of legal services around the world is enormous.", |
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| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "We organized this workshop to bring together researchers and practitioners from around the world who develop NLP techniques for legal data. This is an exciting opportunity to expand the boundaries of our field by identifying new problems and exploring new data as it interacts with the full inventory of NLP and machine learning approaches. In this spirit, the Organizing and Program Committee was assembled to include researchers from both academia and industry, from NLP and legal backgrounds.", |
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| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "We were interested in the following types of papers: (1) applications of NLP methods to legal tasks;", |
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| "ref_spans": [], |
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| "section": "Introduction", |
| "sec_num": null |
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| "text": "(2) experimental results using and adapting NLP methods in legal documents; (3) descriptions of new legal tasks for NLP; (4) creation of curated and/or annotated resources; (5) descriptions of systems which use NLP technologies for legal text; (6) industrial research in this area and (7) interdisciplinary position papers. We offered the option of submitting original unpublished research as non-archival in order to accommodate publication of the work at a later date in a conference or journal. These papers were reviewed following the same procedure as archival submissions. We also offered a venue for presentation for papers accepted to the Findings of EMNLP 2021 on the above topics.", |
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| "section": "Introduction", |
| "sec_num": null |
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| { |
| "text": "We received a record number of 48 submissions and accepted 28 papers for an overall acceptance rate of 58.3% percent, all being presented orally. Out of the 28 accepted papers, 17 are long papers, 7 are short papers and 4 are original work submitted as non-archival. Each paper was reviewed by 3 or 4 members of the Program Committee. The papers cover a wide range of topics from new data sets for legal NLP and Transformer models pre-trained on legal corpora to information retrieval, extraction, question answering, classification, parsing, and summarization for legal documents to legal judgment prediction, legal dialogue, legal reasoning, and ethics.", |
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| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "We thank our two invited speakers for accepting our invitation: John Armour Professor of Law and Finance at Oxford University and Sylvie Delacroix, Professor in Law and Ethics at the University of Birmingham. In the tradition of past NLLP workshops, the invited speakers are legal scholars with an interest in empirical methods for legal analysis including artificial intelligence and NLP methods. We hope their talks offer a fresh perspective for the attendees.", |
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| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "We thank everyone who expressed interest in the workshop, all authors of submitted papers, members of the Program Committee who did an excellent job at reviewing papers given a short turnaround time, everyone attending the workshop, the EMNLP 2021 conference for hosting us and the workshop and publication chairs for their support. We thank our sponsors -Bloomberg and Bloomberg Law -for their contributions.", |
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| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "The NLLP Workshop organizers.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "http://nllpw.org iii", |
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| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": null |
| }, |
| { |
| "text": "Authors: Mikl\u00f3s Seb\u0151k (Centre for Social Sciences, Budapest), Anna Szek\u00e9ly (Centre for Social Sciences, Budapest) and Istv\u00e1n J\u00e1ray (Centre for Social Sciences, Budapest)", |
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| "section": "The Power of Legislatures in Hungary -A Text Reuse Analysis", |
| "sec_num": null |
| }, |
| { |
| "text": "In this paper we shed fresh light on parliaments' \"viscosity\": their ability to withstand government pressure when it comes to passing laws, choosing Hungary as a case of our study. We use state-of-the-art NLP methods (in the subfield of text reuse analysis) to gauge the changes of bill texts. We expect the bill's institutional and intrinsic features and partisan and coalition politics to largely influence the likelihood of bill change. Our results show that international treaties and bills submitted during electoral years are amended less likely. Furthermore, certain policy areas also decrease the likelihood of bill change, especially those which are connected to the core functions of the state: Law and order, defense, international affairs, and government operation.", |
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| "section": "Abstract", |
| "sec_num": null |
| }, |
| { |
| "text": "vii", |
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| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
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| "raw_text": "Automatic Resolution of Domain Name Disputes Wayan Oger Vihikan, Meladel Mistica, Inbar Levy, Andrew Christie and Timothy Baldwin . . . 228 xii Conference Program 08:00-08:10 Workshop Opening 08:10-09:00 Invited Talk John Armour 09:00-09:15 A Corpus for Multilingual Analysis of Online Terms of Service Kasper Drawzeski, Andrea Galassi, Agnieszka Jablonowska, Francesca Lagioia, Marco Lippi, Hans Wolfgang Micklitz, Giovanni Sartor, Giacomo Tagiuri and Paolo Torroni 09:15-09:30 Named Entity Recognition in the Romanian Legal Domain Vasile Pais, Maria Mitrofan, Carol Luca Gasan, Vlad Coneschi and Alexandru Ianov 09:30-09:45 The Power of Legislatures in Hungary -A Text Reuse Analysis Mikl\u00f3s Seb\u0151k, Anna Szek\u00e9ly and Istv\u00e1n J\u00e1ray 09:45-10:00 Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark Joel Niklaus, Ilias Chalkidis and Matthias St\u00fcrmer 10:00-10:15 LexGLUE: A Benchmark Dataset for Legal Language Understanding in English Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopou- los, Daniel Martin Katz and Nikolaos Aletras 10:15-10:30 'Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP Anna Rogers, Tim Baldwin and Kobi Leins 10:30-10:45 Break 10:45-11:00 Automated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language Mohr Wenger, Tom Kalir, Noga Berger, Carmit Klar Chalamish, Renana Keydar and Gabriel Stanovsky 11:00-11:15 A Multilingual Approach to Identify and Classify Exceptional Measures against COVID-19", |
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| "raw_text": "Georgios Tziafas, Eugenie de Saint-Phalle, Wietse de Vries, Clara Egger and Tom- maso Caselli 11:15-11:30 Multi-granular Legal Topic Classification on Greek Legislation Christos Papaloukas, Ilias Chalkidis, Konstantinos Athinaios, Despina Pantazi and Manolis Koubarakis 11:30-11:40 Machine Extraction of Tax Laws from Legislative Texts Elliott Ash, Malka Guillot and Luyang Han xiii 11:40-11:50 jurBERT: A Romanian BERT Model for Legal Judgement Prediction Mihai Masala, Radu Cristian Alexandru Iacob, Ana Sabina Uban, Marina Cidota, Horia Velicu, Traian Rebedea and Marius Popescu 11:50-12:00 JuriBERT: A Masked-Language Model Adaptation for French Legal Text Stella Douka, Hadi Abdine, Michalis Vazirgiannis, Rajaa El Hamdani and David Restrepo Amariles 12:00-12:10 Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector Rajdeep Sarkar, Atul Kr. Ojha, Jay Megaro, John Mariano, Vall Herard and John P. McCrae 12:10-13:00 Lunch Break & Virtual Town Hall 13:00-13:45 Invited talk: Data Trusts as a Bottom-up Empowerment Tool Sylvie Delacroix 13:45-14:00 AutoLAW: Augmented Legal Reasoning through Legal Precedent Prediction Robert Zev Mahari 14:00-14:10 A Free Format Legal Question Answering System Soha Khazaeli, Janardhana Punuru, Chad Morris, Sanjay Sharma, Bert Staub, Michael Cole, Sunny Chiu-Webster and Dhruv Sakalley 14:10-14:20 Searching for Legal Documents at Paragraph Level: Automating Label Generation and Use of an Extended Attention Mask for Boosting Neural Models of Semantic Similarity Li Tang and Simon Clematide 14:20-14:30 GerDaLIR: A German Dataset for Legal Information Retrieval Marco Wrzalik and Dirk Krechel 14:30-14:45 Break 14:45-15:00 SPaR.txt, a Cheap Shallow Parsing Approach for Regulatory Texts Ruben Kruiper, Ioannis Konstas, Alasdair J.G. Gray, Farhad Sadeghineko, Richard Watson and Bimal Kumar 15:00-15:15 Capturing Logical Structure of Visually Structured Documents with Multimodal Transition Parser Yuta Koreeda and Christopher Manning 15:15-15:30 Legal Terminology Extraction with the Termolator Nhi Pham, Lachlan Pham and Adam L. Meyers xiv 15:30-15:45 Supervised Identification of Participant Slots in Contracts Dan Simonson 15:45-16:00 ContractNLI: A Dataset for Document-level Natural Language Inference for Con- tracts Yuta Koreeda and Christopher Manning 16:00-16:10 Named Entity Recognition in Historic Legal Text: A Transformer and State Machine Ensemble Method Fernando Trias, Hongming Wang, Sylvain Jaume and Stratos Idreos 16:10-16:25 Break 16:25-16:40 Summarization of German Court Rulings Ingo Glaser, Sebastian Moser and Florian Matthes 16:40-16:55 Privacy Policy Question Answering Assistant A Query-Guided Extractive Summa- rization Approach Moniba Keymanesh, Micha Elsner and Srinivasan Parthasarathy 16:55-17:10 Learning from Limited Labels for Long Legal Dialogue Jenny Hong, Derek Chong and Christopher Manning 17:10-17:20 Automating Claim Construction in Patent Applications: The CMUmine Dataset Ozan Tonguz, Yiwei Qin, Yimeng Gu and Hyun Hannah Moon 17:20-17:30 Effectively Leveraging BERT for Legal Document Classification Nut Limsopatham 17:30-17:45 Semi-automatic Triage of Requests for Free Legal Assistance Meladel Mistica, Jey Han Lau, Brayden Merrifield, Kate Fazio and Timothy Bald- win 17:45-18:00 Automatic Resolution of Domain Name Disputes Wayan Oger Vihikan, Meladel Mistica, Inbar Levy, Andrew Christie and Timothy Baldwin xv", |
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| } |
| }, |
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