AILA 2019 Precedent & Statute Retrieval Task
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
<strong>Dataset of the AILA (Artificial Intelligence for Legal Assistance) Track at FIRE 2019</strong> Track website : https://sites.google.com/view/fire-2019-aila/<br> Conference website : http://fire.irsi.res.in/fire/2019/home In<strong> </strong>countries following the Common Law system (e.g., UK, USA, Canada, Australia, India), there are two primary sources of law – <em>Statutes</em> (established laws) and <em>Precedents</em> (prior cases). Statutes deal with applying legal principles to a situation (facts / scenario / circumstances which lead to filing the case). Precedents or prior cases help a lawyer understand how the Court has dealt with similar scenarios in the past, and prepare the legal reasoning accordingly. When a lawyer is presented with a situation (that will potentially lead to filing of a case), it will be very beneficial to him/her if there is an automatic system that identifies a set of related prior cases involving similar situations as well as statutes/acts that can be most suited to the purpose in the given situation. Such a system shall not only help a lawyer but also benefit a common man, in a way of getting a preliminary understanding, even before he/she approaches a lawyer. It shall assist him/her in identifying where his/her legal problem fits, what legal actions he/she can proceed with (through statutes) and what were the outcomes of similar cases (through precedents). Motivated by the above scenario, we propose two tasks here : <strong>Task 1 : Identifying relevant prior cases for a given situation</strong> <strong>Task 2 : Identifying most relevant statutes for a given situation</strong> <strong>Task Description:</strong> You will be given a set of 50 queries, each of which describes a situation. <em><strong>Task 1: Identifying relevant prior cases</strong></em> We provide ~3000 case documents of cases that were judged in the Supreme Court of India. For each query, the task is to retrieve the most similar / relevant case document with respect to the situation in the given query. <em><strong>Task 2: Identifying relevant statutes</strong></em> We have identified a set of 197 statutes (Sections of Acts) from Indian law, that are relevant to some of the queries. We provide the title and description of these statutes. For each query, the task is to identify the most relevant statutes (from among the 197 statutes). Note that, the task can be modelled either as an unsupervised retrieval task (where you search for relevant statues) or as a supervised classification task (e.g., trying to predict for each statute whether it is relevant). For the latter, case documents provided for Task 1 can be utilised. However, if a team wishes to apply supervised models, then it is their responsibility to create the necessary training data.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,218 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle