COLIEE-2023 CALL FOR TASK PARTICIPATIONCompetition on Legal Information Extraction/Entailment (COLIEE)COLIEE-2023 Workshop: June, 19 2023Run in association with the International Conference on Artificial Intelligence and Law (ICAIL 2023)Venue: Law School,Centro Algoritimi and LASI, University of Minho (Braga, Portugal)COLIEE registration due: Mar 15, 2023
Those who wish to use previous COLIEE data for a trial, please contact coliee_participation(at)nii.ac.jp Results for all tasks have been released!
The schedule of all COLIEE 2023 talks is now available!
COLIEE 2023 Proceedings now available!
Sponsored by Alberta Machine Intelligence Institute (AMII) University of Alberta National Institute of Informatics (NII) Jurisage Intellicon | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Download Call for ParticipationFour tasks are included in the 2023 competition: Tasks 1 and 2 are about the case law competition, and tasks 3 and 4 are about the statute law competition. Task 1 is a legal case retrieval task, and it involves reading a new case Q, and extracting supporting cases S1, S2, ..., Sn from the provided case law corpus, to support the decision for Q. Task 2 is the legal case entailment task, which involves the identification of a paragraph from existing cases that entails the decision of a new case. As in previous COLIEE competitions, Task 3 is to consider a yes/no legal question Q and retrieve relevant statutes from a database of Japanese civil code statutes; Task 4 is to confirm entailment of a yes/no answer from the retrieved civil code statutes. 1. Tasks Description1.1 (COLIEE Case Law Competition) Task 1:The Legal Case Retrieval TaskThis legal case competition focuses on two aspects of legal information processing related to a database of predominantly Federal Court of Canada case laws, provided by Compass Law.The legal case retrieval task involves reading a new case Q, and extracting supporting cases S1, S2, ... Sn for the decision of Q from the entire case law corpus. Through the document, we will call the supporting cases for the decision of a new case 'noticed cases'. 1.2 (COLIEE Case Law Competition) Task 2: The Legal Case Entailment taskThis task involves the identification of a paragraph from existing cases that entails the decision of a new case.Given a decision Q of a new case and a relevant case R, a specific paragraph that entails the decision Q needs to be identified. We confirmed that the answer paragraph can not be identified merely by information retrieval techniques using some examples. Because the case R is a relevant case to Q, many paragraphs in R can be relevant to Q regardless of entailment. This task requires one to identify a paragraph which entails the decision of Q, so a specific entailment method is required which compares the meaning of each paragraph in R and Q in this task. 1.3 (COLIEE Statute Law Competition) Task 3: The Statute Law Retrieval TaskThe COLIEE statute law competition focuses on two aspects of legal information processing related to answering yes/no questions from Japanese legal bar exams (the relevant data sets have been translated from Japanese to English).Task 3 of the legal question answering task involves reading a legal bar exam question Q, and extracting a subset of Japanese Civil Code Articles S1, S2,..., Sn from the entire Civil Code which are those appropriate for answering the question such that Entails(S1, S2, ..., Sn , Q) or Entails(S1, S2, ..., Sn , not Q). Given a question Q and the entire Civil Code Articles, we have to retrieve the set of "S1, S2, ..., Sn" as the answer of this track. 1.4 (COLIEE Statute Law competition) Task 4: The Legal Textual Entailment Data CorpusTask 4 of the legal textual entailment task involves the identification of an entailment relationship such thatEntails(S1, S2, ..., Sn , Q) or Entails(S1, S2, ..., Sn , not Q). Given a question Q, we have to retrieve relevant articles S1, S2, ..., Sn through phase one, and then we have to determine if the relevant articles entail "Q" or "not Q". The answer of this track is binary: "YES"("Q") or "NO"("not Q"). 2. Data Corpus2.1 Case Law Competition Data Corpus(Task 1 and Task 2)COLIEE-2023 data is drawn from an existing collection of predominantly Federal Court of Canada case law.Participants can choose which phase they will apply for, amongst the two sub-tasks as follows: 1) Task 1: legal information retrieval task. Input is an unseen legal case Q, and output should be relevant cases in the given legal case corpus that support the decision of the input case, which are 'noticed cases'. 2) Task 2: Recognizing entailment between a new case and a relevant case. Input is a decision fragment from an unseen case and a relevant case (the full text from the unseen case, with a few pieces suppressed, is also given as input). Output should be a specific paragraph from the relevant case, which entails the given fragment of the unseen case. 2.2 Statute Law Competition Data Corpus(Task 3 and Task 4)The corpus of legal questions is drawn from Japanese Legal Bar exams, and all the Japanese Civil Law articles are also provided (file format and access described below).Participants can choose which phase they will apply for, amongst the three sub-tasks as follows: 1) Task 3: Legal Information Retrieval Task. Input is a bar exam 'Yes/No' question and output should be relevant civil law articles. 2) Task 4: Recognizing Entailment between Law Articles and Queries. Input is a bar exam 'Yes/No' question. After retrieving relevant articles using your method, you have to determine 'Yes' or 'No' as the output. 3. Measuring the Competition Results3.1. Measuring the Case Law Competition Results (Tasks 1 and 2)For Tasks one and two, evaluation measure will be precision, recall and F-measure:(the number of retrieved cases(paragraphs) for all queries) , Recall = (the number of correctly retrieved cases(paragraphs) for all queries) (the number of relevant cases(paragraphs) for all queries) , F-measure = (2 x Precision x Recall) (Precision + Recall) In the evaluation of Task 1 and Task 2, We simply use micro-average (evaluation measure is calculated using the results of all queries) rather than macro-average (evaluation measure is calculated for each query and then take average). 3.2. Measuring the Statute Law Competition Results (Tasks 3 and 4)For Task 3, evaluation measure will be precision, recall and F2-measure (since IR process is pre-process to select candidate articles for providing candidates which will be used in the entailment process, we put emphasis on recall), and it is:(the number of retrieved articles for each query) , Recall = average of (the number of correctly retrieved articles for each query) (the number of relevant articles for each query) , F2-measure = (5 x Precision x Recall) (4 x Precision + Recall) In addition to the above evaluation measures, ordinal information retrieval measures such as Mean Average Precision and R-precision can be used for discussing the characteristics of the submission results. In COLIEE 2023, the method used to calculate the final evaluation score of all queries is macro-average (evaluation measure is calculated for each query and their average is used as the final evaluation measure) instead of micro-average (evaluation measure is calculated using results of all queries). For Task 4, the evaluation measure will be accuracy, with respect to whether the yes/no question was correctly confirmed: (the number of all queries) 4. Submission detailsParticipants are required to submit a paper on their method and experimental results. At least one of the authors of an accepted paper has to present the paper at the COLIEE workshop of ICAIL 2023 (submit your paper here). Papers should not exceed 10 pages (inclusive of references) and should conform to the standards set out at the ICAIL 2023 webpage, section "Paper Submission", except for the copyright description (you may just delete the copyright description, or claim copyright of your paper by yourself). As post-proceedings, we plan to publish selected papers after another round of reviews at a special issue at "The Review of Socionetwork Strategies" journal, applying the same treatment used for COLIEE 2021 selected papers.Participants should clearly mention what dataset was used (for example: pretrained by Wikipedia dump data as of 2022xxxx, fine-tuned by...) for reproducibility purposes. Participants can use any external data, but it is assumed that they do not use the test dataset and/or something which could directly contain the correct answers of the test dataset. 5. ScheduleJan 17, 2023 Training data release Jan 26, 2023 Test data release Submission deadline: 23:59 AoE for all dates above. 6. Details of Each Task6.1 Task 1 DetailsOur goal is to explore and evaluate legal document retrieval technologies that are both effective and reliable.The task investigates the performance of systems that search a set of case laws that support the unseen case law. The goal of the task is to return 'noticed cases' in the given collection to a query. We call a case is 'noticed' to a query iff the case is referenced by the query case. In this task, the references are redacted from the query case contents, because our goal is to measure how accurately a machine can capture decision-supporting cases for a given case. A corpus composed of Federal Court of Canada case laws will be provided. The process of executing the new query cases over the existing cases and generating the experimental runs should be entirely automatic. All query and noticed cases will be provided as a pool. In the training data, we will also disclose which are the noticed cases for each query case. In the test data, only the query cases will be given and the task is to predict which cases should be noticed with respect to each of the test query cases. There should be no human intervention at any stage, including modifications to your retrieval system motivated by an inspection of the test queries. You won't have access to the test labels before you submit your runs. At most three runs from each group will be assessed. The submission format and evaluation methods are described below. 6.2 Task 2 DetailsOur goal is to predict the decision of a new case by entailment from previous relevant cases.As a simpler version of predicting a decision, a decision of a new case and a noticed case will be given as a query. Then, your legal textual entailment system identifies which paragraph in the noticed case entails the decision, by comparing the meanings between queries and the paragraphs. The task investigates the performance of systems that identifies a paragraph that entails the decision of an unseen case. Training data consists of triples of a query, a noticed case, and a paragraph number of the noticed case by which the decision of the query is entailed. The process of executing the queries over the noticed cases and generating the experimental runs should be entirely automatic. Test data will include only queries and noticed cases, but no paragraph numbers. There should be no human intervention at any stage, including modifications to your retrieval system motivated by an inspection of the test queries. 'Decision', in this context, does not mean the final decision of a case, but rather a conclusion expressed by the judge which is entailed by one or more particular paragraphs from the noticed case. In our dataset, this information is packaged in a file named 'entailed_fragment.txt'. 6.3 Task 3 DetailsOur goal is to explore and evaluate legal document retrieval technologies that are both effective and reliable.The task investigates the performance of systems that search a static set of civil law articles using previously unseen queries. The goal of the task is to return relevant articles in the collection to a query. We call an article as "Relevant" to a query iff the query sentence can be answered Yes/No, entailed from the meaning of the article. If combining the meanings of more than one article (e.g., "A", "B", and "C") can answer a query sentence, then all the articles ("A", "B", and "C") are considered "Relevant". If a query can be answered by an article "D", and it can be also answered by another article "E" independently, we also consider both of the articles "D" and "E" are "Relevant". This task requires the retrieval of all the articles that are relevant to answering a query. Japanese civil law articles (English translation besides Japanese) will be provided, and training data consists of pairs of a query and relevant articles. The process of executing the queries over the articles and generating the experimental runs should be entirely automatic. Test data will include only queries but no relevant articles. There should be no human intervention at any stage, including modifications to your retrieval system motivated by an inspection of the queries. You should not materially modify your retrieval system between the time you downloaded the queries and the time you submit your runs. At most three runs from each group will be assessed. The submission format and evaluation methods are described below. 6.4 Task 4 DetailsOur goal is to construct Yes/No question answering systems for legal queries, by entailment from the relevant articles.If a 'Yes/No' legal bar exam question is given, your legal information retrieval system retrieves relevant Civil Law articles. Then, the task investigates the performance of systems that answer 'Yes' or 'No' to previously unseen queries by comparing the meanings between queries and your retrieved Civil Law articles. Training data consists of triples of a query, relevant article(s), a correct answer "Y" or "N". Test data will include only queries and relevant articles, but no 'Y/N' label. There should be no human intervention at any stage, including modifications to your retrieval system motivated by an inspection of the queries. You should not materially modify your retrieval system between the time you downloaded the queries and the time you submit your runs. At most three runs for each group should be assessed. The submission format and evaluation methods are described below. 6.5 Important NoticeParticipants should clearly mention what dataset was used (for example: pretrained by Wikipedia dump data as of 2022xxxx, fine-tuned by...) for reproducibility purposes. Participants can use any external data, but it is assumed that they do not use the test dataset and/or something which could directly contain the correct answers of the test dataset.7. Corpus StructureThe structure of the test corpora is derived from a general XML representation developed for use in RITEVAL, one of the tasks of the NII Testbeds and Community for Information access Research (NTCIR) project, as described at the following URL:http://sites.google.com/site/ntcir11riteval/ The RITEVAL format was developed for the general sharing of information retrieval on a variety of domains. 7.1 Case Law Competition Corpus Structure - Task 1The corpus is given as a flat list of files containing all query and noticed cases, for both the training and test datasets. The training dataset is described in a json file containing a mapping between the query case and a list of noticed cases, as in the example below:
7.2 Case Law Competition Corpus Structure - Task 2The corpus structure for Task 2 is given below:
task2_training_corpus
+--- 001
+------- base_case.txt
+------- entailed_fragment.txt
+------- paragraphs
+----------- 001.txt
+----------- 002.txt
+----------- ...
+----------- 046.txt
+--- 002
+------- base_case.txt
+------- entailed_fragment.txt
+------- paragraphs
+----------- 001.txt
+----------- 002.txt
+----------- ...
+----------- 211.txt
+--- train_labels.json
For the query case 001, there are 46 paragraphs in the referenced case (among which is the expected answer, 013.txt, as given in the golden labels JSON file shown before). For the query case 002, there are 211 paragraphs in the referenced case, among which are the two which entail the fragment of text for that case (003.txt and 045.txt, as given in the golden labels file).
For the case whose id is "001", the expected answer is "013.txt", meaning the entailed fragment (ie, the decision) in that query can be entailed from the paragraph id 013 in the given noticed case. The decision in the query is not the final decision of the case. This is a decision for a part of the case, and a paragraph that supports this decision should be identified in the given noticed case. The test corpora will not include the JSON file mapping, and the task is to predict which paragraph(s) entail(s) the decision given by the entailed_fragment.txt file in each case. 7.3 Statute Law Competition Corpus Structure (Tasks 3 and 4)The format of the COLIEE competition corpora derived from an NTCIR representation of confirmed relationships between questions and the articles and cases relevant to answering the questions, as in the following example:
For the Tasks 3 and 4, the training data will be the same. The groups who participate only in the Task 3 can disregard the pair label. 8. Competition Results Submission Format8.1. Task 1For Task 1, a submission should consist of a single ASCII text file. Use a single space to separate columns, with three columns per line as follows:000001 000018 univABC 000001 000045 univABC 000001 000130 univABC 000002 000433 univABC . . .where: 1. The first column is the query file name. 2. The second column is the official case number of the retrieved case. 3. The third column is called the "run tag" and should be a unique identifier for the submitting group, i.e., each run should have a different tag that identifies the group. Please restrict run tags to 12 or fewer letters and numbers, with no punctuation. In this example of a submission, you can see that 000001 has multiple relevant articles (000018.txt, 000045.txt and 000130.txt). 8.2. Task 2For Task 2, a submission should consist of a single ASCII text file. Use a single space to separate columns, with three columns per line as follows:001 013 univABC 002 037 univABC 002 002 univABC 003 008 univABC . . .where: 1. The first column is the query id. 2. The second column is the paragraph number which entails the decision. 3. The third column is called the "run tag" and should be a unique identifier for the submitting group, i.e., each run should have a different tag that identifies the group. Please restrict run tags to 12 or fewer letters and numbers, with no punctuation. A query can have multiple entailing paragraph numbers. 8.3. Task 3Submission format in Task 3 is the TREC eval format used in trec_eval program. Use a single space to separate columns, with six columns per line as follows:H21-5-3 Q0 213 1 0.8 univABC Where 1. The first column is the query id. 2. The second column is "iter" for trec_eval and not used in the evaluation. Information of the column will be ignored. But please write Q0 in this column. 3. The third column is the official article number of the retrieved article. 3. The fourth column is the rank of the the retrieved articles. 3. The fifth column is similarity value (float value) of the retrieved articles. 6. The sixth column is called the "run tag" and should be a unique identifier for the submitting group, i.e., each run should have a different tag that identifies the group. Please restrict run tags to 12 or fewer letters and numbers, with no punctuation. Please refer to the README file of the trec_eval.8.1.tar.gz for detailed explanation. Most significant difference between the previous submission format and new one is that it is necessary to provide ranked lists instead of simple answer sets. Maximum numbers of the documents for each query is limited to 100. It is also encouraged to submit ranked list results with 100 candidates for each query. Since such submissions have smaller precision values due to the large numbers of candidates, it may be inappropriate to compare ones with small numbers of candidates. In order to clarify these different types of submissions, please add suffix "-L" for the submission result file (e.g., When univABC is the results for the submission with limited numbers of candidates, please use univABC-L for the submission with large numbers of submission). 8.4. Task 4For Task 4, again a submission should consist of a single ASCII text file. Use as single space to separate columns as follows, with three columns per line as follows:H18-1-2 Y univABC H18-5-A N univABC H19-19-I Y univABC H21-5-3 N univABC . . .where: 1. and 3 as for Phase One, 2. "Y" or "N" indicating whether the Y/N question was confirmed to be true ("Y") by the relevant articles, or confirmed to be false ("N"). Participants are also required to submit answers and evaluation results of past three years' formal run settings, i.e. using each of the past three years' datasets (H30,H31,R01) as test datasets, older years' datasets (-H29, -H30,-H31) as training datasets. If this is not realistic due to e.g. the training time, please consult the task organizers. In your submission, please add the dataset name as a prefix to the original file name: R03.task4.YOURID Final submission for TestData_{jp,en}.xml R02.task4.YOURID for riteval_R02_{jp,en}.xml R01.task4.YOURID for riteval_R01_{jp,en}.xml H30.task4.YOURID for riteval_H30_{jp,en}.xml 9. Presentation ScheduleTimes below in Portuguese Summer Time (GMT+1). 09:00-09:30 Summary Paper (30min) 09:30-10:00 Haitao Li, Weihang Su, Changyue Wang, Yueyue Wu, Qingyao Ai and Yiqun Liu, THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Efficient Legal Case Retrieval (Champion paper for Task1) 10:00-10:45 Chau Nguyen, Phuong Nguyen, Thanh Tran, Dat Nguyen, An Trieu, Tin Pham, Anh Dang and Le-Minh Nguyen, CAPTAIN at COLIEE 2023: Efficient Methods for Legal Information Retrieval and Entailment Tasks (Champion paper for Task2 and Task3) 10:45-11:00 Coffee Break 11:00-11:30 Quan Minh Bui, Dinh-Truong Do and Le-Minh Nguyen, JNLP @COLIEE-2023: Data Augmentation and Large Language Model for Legal Case Retrieval and Entailment(Champion paper for Task4) 11:30-12:00 Luisa Novaes, Daniela Vianna and Altigran da Silva, A Topic-Based Approach for the Legal Case Retrieval Task (task1) 12:00-12:30 Thi-Hai-Yen Vuong, Hai-Long Nguyen, Tan-Minh Nguyen, Hoang-Trung Nguyen, Thai-Binh Nguyen and Ha-Thanh Nguyen. NOWJ at COLIEE 2023 - Multi-Task and Ensemble Approaches in Legal Information Processing (task1) 12:30-13:30 Lunch 13:30-14:00 Rohan Debbarma, Pratik Prawar, Abhijnan Chakraborty and Srikanta Bedathur. IITDLI : Legal Case Retrieval Based on Lexical Models (task1) 14:00-14:30 Mi-Young Kim, Juliano Rabelo, Randy Goebel and Housam Babiker. Transformer-based Legal Information Extraction (task1) 14:30-15:00 Haitao Li, Changyue Wang, Weihang Su, Yueyue Wu, Qingyao Ai and Yiqun Liu. THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment (task2) 15:00-15:15 Cofee Break 15:15-15:45 Michel Custeau and Diana Inkpen. Performance of Individual Models vs. Agreement-Based Ensembles for Case Entailment (task2) 15:45-16:15 Takaaki Onaga, Masaki Fujita and Yoshinobu Kano. Japanese Legal Bar Problem Solver Focusing on Person Names (task4) 16:15-16:45 Masaharu Yoshioka and Yasuhiro Aoki. HUKB at COLIEE 2023 Statute Law Task (task4) 16:45-17:15 Onur Bilgin, Logan Fields, Antonio Laverghetta Jr., Zaid Marji, Animesh Nighojkar, Stephen Steinle and John Licato. AMHR Lab 2023 COLIEE Competition Approach (task4) 10. Task WinnersThe list of winners for the COLIEE 2023 edition is:
11. Application DetailsPotential participants to COLIEE-2023 should respond to this call for participation by submitting an application. To apply, submit the application and memorandums of the following URLs to coliee_participation(at)nii.ac.jp:
Questions and Further Informationcoliee_participation(at)nii.ac.jpWe will send an acknowledgement to the email address supplied in the form once we have processed the form. COLIEE 2023 ProceedingsThe complete proceedings for the 2023 COLIEE edition are available here.Previous COLIEE editionsCOLIEE 2022. A summary paper on the 2022 edition is available.COLIEE 2021. A summary paper on the 2021 edition is available, as well as the complete proceedings. COLIEE 2020. A summary paper on the 2020 edition is available. COLIEE 2019. A summary paper on the 2019 edition is available. COLIEE 2018. Summary papers on the case law tasks and statute law tasks available. COLIEE 2017 COLIEE 2016 COLIEE 2015 COLIEE 2014 Program CommitteeRandy Goebel, University of Alberta, Canada Yoshinobu Kano, Shizuoka University, Japan Mi-Young Kim, University of Alberta, Canada Maria Navas Loro, Technical University of Madrid, Spain Nguyen Le Minh, JAIST, Japan Juliano Rabelo, University of Alberta, Canada Julien Rossi, University of Amsterdam, The Netherlands Ken Satoh, NII, Japan Jaromir Savelka, University of Pittsburgh, USA Yunqiu Shao, Tsinghua University, China Akira Shimazu, JAIST, Japan Satoshi Tojo, JAIST, Japan Vu Tran, JAIST, Japan Josef Valvoda, Cambridge University, UK Hannes Westermann, University of Montreal, Canada Hiroaki Yamada, Tokyo Institute of Technology, Japan Masaharu Yoshioka, Hokkaido University, Japan Sabine Wehnert, Otto-von-Guericke-Universität Magdeburg, Germany | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Last updated: April, 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||