dataesr/scientific-paragraphs-categorization huggingface dataset:
Related Work There are a few API implementations specifically targeting the response to data subject right-related requests, however, they only focus on providing a copy of the personal data and leave out all the other requirements specified by the GDPR. Microsoft Graph  [3]  is a platform developed to access Microsoft 365 data. In particular, the Microsoft Graph compliance and privacy APIs  [4]  were developed to create and manage data subject access requests and to help developers and enterprises to easily identify data subjects and find their personal information. Although the process through which the data is obtained is automated, several components require manual intervention, such as confirmation as to whether the data is being processed. Oracle's Data Privacy API focus on providing a solution for enterprises that use Oracle databases to store personal data  [5].
API development The main technologies used to implement the API were the expressjs 5  and swagger-ui-express 6  libraries, used to develop the API and create its respective documentation. Inrupt's JavaScript client libraries  7  were also used to authenticate the user and to handle data stored in a Solid Pod. Initially, the developed API only had one parameter which was related to the identity of the user as this is the only requirement described by the GDPR for a data subject to be allowed to exercise their right of access. However, since the data subject may, for example, be interested in accessing only certain categories of data or only accessing data used for a certain purpose, data categories and purposes were added as request parameters to allow the data subject to have a more fine-grained access right.
For the purposes of this work, we assume that users are using the Open Digital Rights Language (ODRL) Profile for Access Control (OAC)  [7]  to create policies to govern the access to their Solid-stored data  9. Using these policies the API method matches the request purpose with the stored policies' purposes and if there is a match then the corresponding data is returned. In addition to the policies, we assume that consent records, corresponding to granted data access requests, are kept in the Pod. The generation and modelling of consent records are based on previous work  [8]. These records are then used by the API to retrieve the entities that accessed the data. A public repository with the developed code is accessible at https://github. com/besteves4/access-right-api.
As the users need to be able to select specific types of personal data and/or specific purposes for the processing of said data to have a more fine-grained right of access, the developed Solid application includes two drop-down trees that use DPV's personal data categories and purposes taxonomies to populate its structure. The selected categories are then used to feed the API request call. For each returned resource, the URI is provided, as well as the category of personal data included in the file, the agents that accessed the data and a list of the policies governing the access to said resource. A download button is also available so that the user can obtain a copy of the resource data. A demonstration of the developed Solid application is available at https://protect. oeg. fi. upm. es/access-right/ and the public repository of the code is accessible at https://github.
[8] ); and machine-learning based, where a dataset of evidence related to usually both compliant or non-compliant activity is used to train a non-compliance classifier over the entire behavioural space  [4]. Unfortunately, sample datasets of fraudulent behaviour suffer from class imbalance; there is only a relatively small amount of labeled instances of non-compliance compared to labeled instances of compliance. To face this issue, recent contributions have proposed to use either inductive (using both labeled and unlabeled instances in the training process)  [3]  or transductive (building upon local similarities among data points)  [6]  semi-supervised learning for non-compliance classification. We propose a research direction that aims to combine recent trends into one simulation framework, where instances of non-compliance can be generated  [5].
2 ocally optimal forms Under hard constraints of the action space, the fraudulent agent can be seen as optimizing the utility function to discover sequences that yield high utility. Fraud schemes as such do not relate only to a specific type of behaviour, but to a class of behaviours that follows a certain higher-level behavioural pattern. Intuitively, one can expect that each scheme might have its representative form, that is, a form that illustrates the essence of a particular fraud scheme. With these representative forms, one does not need to list all the instances of Γ, achieving a compression of possibly infinite set Γ into a finite number of classes. We speak of sequences that attain a local maximum as locally optimal forms. Exploration We consider as given an initial dataset D consisting of behavioural instances labeled as fraudulent, non-fraudulent or unknown.
[8] , who predicted violations of some articles of the European Convention on Human Rights, using a dataset of 584 European Court of Human Rights decisions using Support Vector Machine (SVM), Bag-of-Words (n-grams) and topical features and later by  [9] , who expanded said dataset to obtain a higher performance. Several works focused on national case law. For example,  [10]  applied a linear SVM classifier trained on lexical features to predict the legal area and the outcome of cases by the French Supreme Court. [11]  used logistic regression and SVM to predict the outcomes of Bavarian court decisions. Chinese case law was addressed by  [12, 13, 14]  among others. To the best of our knowledge, this is the first study on outcome prediction of Italian decisions, and also the first one in the VAT domain.
The corpus The source corpus consists of 226 Italian second-instance decisions on Value Added Tax (VAT) by the Regional Tax Commissions from various judicial districts. 3 The decisions, downloaded from the Giustizia Tributaria database,  4  range from 2010 to 2022 and concern taxable transactions, exemptions, out-of-scope transactions, and the right to obtain a deduction. They contain 303 first-instance requests, of which 84 rejected, 126 upheld, and 5 with other outcomes, and 490 second-instance requests, of which 129 rejected, 99 upheld, and 22 with other outcomes. The number of requests is higher than the number of outcomes since a decision on a particular request may imply the uphold or rejection of other requests, which thus are not explicitly addressed. We chose to focus on VAT Italian cases since: (a) though some AI applications exist within the Italian Tax Administration, they do not yet address the case law; (b) VAT is harmonised at the European level, governed by the VAT Directive (Directive 2006/112/EC); 5  (c) the CJEU case law on this matter favours the uniform and consistent interpretation of legal norms, principles and concepts; (d) VAT is a relatively narrow a self-contained branch of the law; (e) Italian VAT decisions have a rather consistent structure; (f) they affect -apart from lawyersaccountants, public servants and millions of taxpayers; (g) we have domain expertise.
Our final dataset is composed of 112 rejected decisions and 71 uphold decisions. Our aim is to predict the court's decisions on the basis of the information provided by the parties before the case. Such information are partially present in the decision, which reports the parties' request(s), claim(s), and argument(s). Nonetheless, in this study, we are also interested in assessing which part of the document can provide a valuable contri- bution to predicting the outcome. Since the justification and decision sections may hold important information about the outcome of a case, we decided to include those sections in the experiments, obtaining four experimental settings. In the first one, the inputs are req, args, and claims; the second and third are similar, but, respectively, also mot and dec are included; the fourth uses both mot and dec.
Results Tables 2 shows the results obtained through each combination of embeddings and classifiers in each setting, as well as two baselines (random and majority class). We measure the F1 score obtained for each class and their macro-average. The task of determining the decision outcome based only on the claims and arguments of the parties reaches a maximum score of 0. 68 with Linear SVC and SBERT. The use of SBERT embeddings instead of TF-IDF is beneficial with all classifiers, leading to results not worse than the baselines. Overall, the Linear SVC classifier seems to give the best result in almost all the settings. There is a wide gap between the scores obtained in the two classes, which we speculate may be caused by their unbalanced distribution in the dataset. The justification section seems to give conflicting results: it slightly worsens the performance of Linear SVC, but it improves other classifiers.
Conclusion Ideally, one would aim to predict the decision of the court based on the information provided by the parties before the case. Our experiments approximate the ideal setup, by focusing on outcome prediction based on fragments in the narrative provided by courts, which we captured through the requests, claims, and arguments marked elements. To this end, we built a first-of-a-kind dataset, on Italian decisions and on the VAT domain. In the future, we plan to include information provided by the parties before the case. From the machine learning viewpoint, we plan to adopt oversampling or augmentation to balance the distribution of the classes in the dataset, and we are investigating more advanced neural architecture for classification or domain-specific embeddings. The Effectiveness of Bidirectional Generative Patent Language Models Jieh-Sheng LEE 1  National Yang Ming Chiao Tung University School of Law ORCiD ID: Jieh-Sheng Lee https://orcid.
Introduction Large language models have achieved notable success in natural language generation (NLG) tasks in recent years. Until now, very few language models have been dedicated to the patent domain. Furthermore, most language models are autoregressive by predicting the next token after having read all the previous ones. Very few language models work backward by predicting the previous token. In this manuscript, the author pre-trained and fine-tuned large language models dedicated to the patent domain. The model can predict the previous token, in addition to predicting the next token. Predicting the previous token is implemented by reversing the tokens for both inputs and outputs. The training dataset also contains tokenized sequences in both forward and backward directions. By doing so, the patent-specific language models in this manuscript can generate patent text in both forward and backward directions.
Dataset and Model sizes The dataset in this manuscript is the USPTO TACD dataset in  [1]  which includes titles, abstracts, claims, and descriptions in granted patents. The coverage of the dataset ranges from 1976 to 2021 (1976∼2020 for training and 2021 for validation). For further details, please refer to  [1]. The model sizes experimented in this manuscript are 6B, 1. 6B, and 456M. These three model sizes outperform others (279M, 191M, 128M, and 115M) in  [1]. After publication, the pre-trained and fine-tuned models in this manuscript will be released. The model configuration in this manuscript reuses  [1]. In terms of training from scratch, this manuscript uses the original GPT-J-6B model and its source code available at  [2].
Keywords. Rule classification, Norms, Legal Knowledge Representation, AI&Law The ability to automatically detect deontic rules directly from natural language sentences is a crucial long-term goal in the field of Artificial Intelligence and Law (AI&Law), and in legal argumentation  [1, 2]. One of the obstacles of this kind of tasks is the lack of available data designed ad hoc for the classification of deontic rules. Since the annotation of this kind of datasets is time-consuming and requires experts of domain, the process of creating datasets to automatically recognize deontic rules can be costly. Another obstacle, related to the first one, is that datasets might be too small to train Machine Learning classifiers, especially when dealing with deep neural architectures. To tackle these issues, a groundbreaking methodology has recently been employed in AI (and NLP), namely Transfer Learning, an approach where huge pre-trained neural architectures are employed in downstream tasks.
In this regard, BERT is one of the most famous examples, used in many downstream tasks even with very small datasets  [3, 4]. On the one side, this work shows the potential of using LegalXML documents as source of data. On the other side, it exploits the ability of Transfer Learning to have good performances on downstream tasks even when dealing with small datasets. Furthermore, this work tackles the automatic classification of deontic rules directly from natural language, an AI&Law task which has been approached by the community only marginally. This task consists in classifying single legal sentences or single legal provisions as containing deontic modalities such as Obligations, Prohibitions and Permissions. 1 Corresponding Author: Davide Liga, davide. liga@uni. lu.
Methodology We consider two methodological aspects: the data extraction method (how we retrieved our data) and the classification method (what Machine Learning approach we used). To extract labelled data for the classification of rules and deontic modalities, we combined Akoma Ntoso and LegalRuleML as suggested in  [5]. While LegalRuleML is an optimal representation of the legal logical sphere, Akoma Ntoso is an optimal representation of the structure of legal document, including their natural language. This can facilitate the reconstruction of the atomic legal provisions from natural language sentences, especially in those cases where the deontic information is split in different structural portions within the legal source. In this work, these two formats have been used to create a dataset, where atomic legal provisions are taken (and sometimes reconstructed) from Akoma Ntoso, while the logical/deontic classes are extracted from LegalRuleML.
Data The data used in this study consists of 707 atomic normative provisions 2 extracted from the European General Data Protection Regulation (GDPR). To extrapolate this dataset, we used the DAta Protection REgulation COmpliance (DAPRECO) Knowledge Base  [15] , which is the LegalRuleML representation of the GDPR and the the biggest knowledge base in LegalRuleML  [16] , as well as the biggest knowledge base formalized in Input/Output Logic  [17]. The current version of the DAPRECO 3 includes 966 formulae in reified Input/Output logic: 271 obligations, 76 permissions, and 619 constitutive rules. As explained in  [15] , the number of constitutive rules is much higher than permissions and obligations because constitutive rules are needed to trigger special inferences for the modelled rules. This means that constitutive rules are an indicator of the existence of a rule, without giving information about deontic modalities.
The class "none" is referred to all sentences which have no rule at all. These labels allowed 4 different experimental settings, as shown in Table  1 : Scenario 1 is a binary classification and aims at discriminating between rule and non-rule instances. In this scenario, all labels other than "none" are considered rule, while 2 These provisions belong to the body of the GDPR (preamble and conclusions were excluded), and are generally paragraphs or list points, which may sometimes consist of multiple sentences. 3 The DAPRECO knowledge base can be freely downloaded from: https://github. com/dapreco/ daprecokb/blob/master/gdpr/rioKB_GDPR. xml. "non-rule" is just an alias for "none". Scenario 2 focus on a binary classification between deontic instances (i. e. , any sentence labelled as either "obligation" or "permission") and non-deontic instances (i.
As far as the experimental settings are concerned, the dataset was divided into 70% for the training phase, 15% for the test and 15% for the validation; and for all instances, a max length of 30 was applied. Regarding the Transfer Learning architecture, 3 pre-trained language model have been fine-tuned, namely BERT  [3] , DistilBERT  [18] , and the LegalBert trained on Eu-rLex  [19]. These three neural architectures were fine-tuned by adding two linear layers with a ReLu activation function and with a dropout of 0. 2 after each activation, and a final output layer was added for the classification, through a softmax activation function. The fine-tuning process of these 3 neural architectures was performed in 10 epochs (learning rate: 1e-3; batch size: 32). The final results on the validation set are reported in Table  3 , where it can be seen that DistilBERT outperforms the other classifiers in the binary classifications, with an average score reaching.
Labeling the Data We can label the categories of our data in two different ways. The most common way is to ask domain professionals to categorize the individual statements. We refer to this dataset as DE, where "D" and "E" denote "Domain" and "Expert", respectively. We used 80% and 20% of DE for training and testing, respectively. We refer to these subsets as DEA and DEE, respectively, where the ending "A" and "E" are for "trAin" and "tEst", respectively. 1. Dist. of the number of paragraphs. The main annotator owns a college degree in law, and we have only limited data for inter-rater agreement at this stage. An intriguing alternative is to take advantage of the regularities of the writing styles in the judgment documents. It is possible to find common patterns of collocations that are indicative of the high-level functions of the paragraphs.
Hence, we relied on specific keywords, phrases, and collocations to algorithmically label the paragraphs to bootstrap our classification tasks, and treated all sentences in a labeled paragraph to belong to the category of the paragraph. Paragraphs for categories C1 and C2 usually begin with "聲請人" and "相對人", respectively. It is very common for the courts to use "按" in the beginning statements in paragraphs of category C3. The collocations that follow the regular expression "(本|法) 院. * (判斷|心證|經查|據)" strongly suggests paragraphs of category C4. We refer to this algorithmically labeled dataset as KP, where "K" and "P" denote "Keywords" and "Patterns", respectively. Again, we used 80% and 20% of KP for training and testing, respectively. We will refer to these subsets of data as KPA and KPE, respectively, for analogous naming principle.
7. 2 Testing with KPE Table  7  shows only the values of the macro F 1 when we tested two classifiers that were trained by DEA and by KPA and test both classifiers with KPE. Since we already had human annotated data, this comparison is only of theoretical interest. Even if the KPA and the KPE datasets were labeled with the same principles, the statistics in Table  7  indicate that using DEA to train can still help us achieve slightly better qualities than when we train the classifiers with KPA.
Here, we present a legal support system for autonomous vehicles (LeSAC ), as shown in Figure  1. It is built on top of legal ontology and a legal reasoning based legal argumentation framework, i. e. L-ASPIC  [5] , adapted from  [6]. Legal ontologies have proved to be very strong tool for law as legal expert systems, legal databases and documents management: There are different functions that have been proposed, e. g. the Legal Knowledge Interchange Format (LKIF) Core ontology builds on the Web Ontology Language (OWL) and rules  [7] ; LRI-Core aimed at the legal domain grounded in common-sense  [8]  and UOL  [9]. And there are legal ontology models constructed for specific legal domains like ALLOT  [10]  etc. However, a legal ontology alone is insufficient for legal reasoning. The main description language of the current legal ontology is the Web Ontology Language (OWL) or OWL2, whose semantics are based on DL  [11]  and they cannot support inconsistent reasoning as a subset of first-order logic, which is quite important in legal reasoning.
Related work Beside generic efforts in event extraction such as the carried out by temporal taggers following TimeML  [1, 2]  or related tasks such as frame-semantic parsing  [3, 4] , semantic 1 Corresponding Author: María Navas-Loro, Ontology Engineering Group, Universidad Politécnica de Madrid, Spain; E-mail: mnavas@fi. upm. es. This work was funded partially by the project Knowledge Spaces: Tecnicas y herramientas para la gestion de grafos de conocimientos para dar soporte a espacios de datos (Grant PID2020-118274RB-I00, funded by MCIN/AEI/10. 13039/501100011033) and by H2020 MSCA PROTECT (813497). role labeling (SRL)  [5, 6]  or open information extraction 3  , some proposals have been made specifically in the legal domain. These works often involve ad hoc definitions of events, ignoring general event annotation schemes.
FrameNet training It is straightforward that some events not present in the training set of the EventsMatter corpus should be detected in other documents, and even that events considered not relevant in those documents can be relevant in other cases. This is why, in addition to the events gathered from the training set explained previously, we decided to enrich the system with frames from FrameNet  [15]. FrameNet is a database that contains semantic frames together with the words that represent them in text, as well as additional information such as the arguments this frame can present. Since frames represent situations, they can be understood as events to some extent, and incorporating a selection of them to our target events would help to generalize our approach. Since not all the frames in FrameNet are of interest, we manually inspected the database using the FrameGrapher tool  6  , that allowed us to navigate through it and find the most relevant frames to our task.
Dataset Creation The key requirement of the dataset was to find legal texts that was rich with the citations. Judges who wants to use laws as a reference in their judgements generally cites cases and statutes around the legal issues that the judgements deals with, hence we decided to include paragraphs of judgements that has citations in it. Since the Supreme Court of India is considered to be the highest authority and is cited multiple times through out all the courts we decided to include only judgments passed by this court. To prepare the dataset, we considered more than 55000 supreme court cases 3  from the year 1947 to 2021. Despite India having many statutory laws at central and state level, we have included only 1260 statutes as these were passed by central government having effect all over India.
Also, each volunteer was then asked to review the annotations of other volunteers to confirm the quality of the dataset. We decided not to go for an Inter-annotator agreement as the task of finding citations is of elementary level for legal volunteers. Then using our proprietary database, each cited case and statute is given its ID using pattern matching with help of the name, year of judgment (in case of precedent law), and enactment(in case of statute law). The citations are not spelled uniformly throughout the corpus; for example, the cited case could be Maneka Gandhi v. Union of India. But the annotated title of this cited case is Maneka Gandhi vs UOI. To normalize the text, we use fuzzywuzzy  6  string matching algorithm to match the annotated citation with the actual document name from a lookup table and replace it with the corresponding ID.