dataesr/scientific-paragraphs-categorization huggingface dataset:
The multilinguality issue is one of the challenges for the abusive language detection task in past years. In fact, most popular social media, such as Twitter, Facebook, and Instagram, go multilingual, fostering their users to interact in their primary language. Therefore, there is a considerable urgency to develop a robust approach for abusive language detection in a multilingual environment  [12] , as well as for supporting better compliance to government demands for counteracting the hate speech phenomenon (e. g. , see the recently issued EU Commission's Code of Conduct on Countering Illegal Hate Speech Online  [7] ). Even if most studies so far have focused on English, the availability of abusive language datasets developed for different languages in past 2 years, including Italian,  [3, 8, 16] , Spanish  [1, 8] , and German  [19] , constitutes an excellent condition for progress on this front.
The model was successfully evaluated against different state-of-theart approaches for hate speech detection based on available benchmark datasets annotated for hate and offensiveness. A use case related to a real hate event in the United States is presented to show the functionality of the MANDOLA framework and visualization dashboard in action. The article tackles a very important problem by combining, in a novel and interesting way, natural language processing and machine learning techniques for hate speech detection with big data analysis and visualization techniques. Mandola's data collection engine is built to be compliant with new General Data Protection Regulations, and processes information in real time and on the fly without storing any user-specific information (apart from the statistical output and metadata resulted from the processing).
The effectiveness of deep learning techniques remains open, as in this study it turned out to be less effective than other simpler approaches due to the limited size of the dataset. As the approaches tested in this article originally were developed for the English language, their use for a different language provides a reference method for their adaptation to other languages, including the creation of a lexicon for the target language from available resources. Despite the portability of the proposed approach across languages, the authors warn about the need to account for the language-specific phenomenology of the hate speech. To do so, they report some significant features of abusive language in Spanish, which shows a larger variety of expressions than English, partly determined by the way opposite polarities can be combined in Spanish to form complex expressions.
by a grant overseen by the  French National Research Agency  ( ANR17-MAOI ) and by the European project  H2020 ANIMATAS (MSCA-ITN-ETN 7659552 ). The editorial work of  V. Patti  was partially funded by  Progetto di Ateneo/CSP   2016  ( Immigrants, Hate and Prejudice in Social Media ,  S1618_L2_BOSC_01 ). P. Rosso was partially funded by  Spanish MICINN  under the research project  MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech  ( PGC2018-096212-B-C31 ).
Materials and Methods The research was carried out during the 2017-2018 winter season, from 29 November 2017 to 15 April 2018, with a total of 138 days. For certain analyses, we used also the data collected during the previous season (2016-2017), which were divided into two periods: From 19 November to 17 April, and from 21 April to 27 April, with three days break because of the lack of snow. This season lasted 157 days (see Table  1 ): For this research we took into account 225 ski slopes from 69 ski areas of Romanian Carpathians Mountains, representing more than 80% of the total number of existing ski slopes in the country. The ski runs technically closed have not been included in the study. The research design has been structured into two stages. The first stage was focused on the ski slopes characteristics, starting altitude, and arrival altitude, collected from "Romania turistica" website, specialized in snow cover information  [37].
Furthermore, the presence or absence of snow cannons has been taken into consideration. In the second stage the status of the ski slopes, slope conditions and snow depth were collected daily, during the 157 days of the 2016-2017 winter season and 138 days of the 2017-2018, from the same website, for each functional ski slope included in the study. Each day was archived in a Word document, as text or snipped images, and then recorded in an SPSS file. The variable status of the ski slopes was presented on the website with three categories: Open, open-impracticable, and closed. The open-impracticable slopes had some damaged areas because the quality of the snow was affected by warming weather. Consequently, for some statistics calculations, these ski slopes have been considered together with the closed ones.
This kind of scale is used in other countries, too, with more or less categories. Closed, impracticable, and wind were considered together for some statistics calculations. The average, maximum, and minimum values, by month, have been calculated for each category. The snow depth has been registered from the website only for the open ski slopes. Additionally, we calculated the average, the maximum, and the minimum of days that the snow depth had the highest value during the season. Two parameters regarding the length of the ski season for a ski area have been considered: 1. The number of days that at least one ski slope was open in a ski area; 2. The number of days that a ski area has been operated at maximum capacity (all ski slopes of this ski area were open). We created the first dataset, containing the following variables (see Table  2 ): On the basis of the collected data, we performed a univariate and bivariate statistical analysis using SPSS Statistics 22.
The Snow Depth and the Ski Slope Conditions The data collected for the variable ski slope conditions, presented on the website as an ordinal scale, were associated with snow depth. We calculated the maximum and minimum values for each category of the scale, highlighting several intervals. These values are presented in Table  12 , in the following ascending order: Analyzing the data from Table  12 , we notice that the ski slopes were open if the natural snow depth was minimum 15 cm, with the snow cannons adding crop snow if necessary. The highest snow depth value recorded for each functional ski slope during the season was maintained on average 22. 6 days per season. As depicted in Table  13 , 55. 43% of functional ski slopes have maintained the highest value of snow depth less than 20 days per season, while only 21.
Participants with structural anomalies (n = 8), an incomplete task fMRI scan (n = 5), excessive head motion during the task fMRI scan (n = 74; defined as more than 20% of frames with a framewise displacement (FD) > 0. 5 mm; Jenkinson et al. [38] ), incomplete information (n = 11), and/or corrupted datasets due to technical failure (n = 16) were excluded. This resulted in the inclusion of 394 individuals, 205 individuals with ASD and 189 TD individuals, in our analyses (see Table  1  for an overview over key descriptive variables for the full sample, and Additional file 1: Table  S1  for a sample description split by age group). Standard operation and quality control procedures are detailed in Additional file 1.
Participants were familiarized to the task in a standardized training session before scanning, including three practice video clips. Task performance during the fMRI scan was evaluated in terms of overall categorization accuracy and accuracy of ToM video categorization (see Additional file 1 for additional age group-specific analyses). Analyses were performed in SPSS (IBM, version 22) using univariate analyses of covariance to assess the effects of diagnosis and age while controlling for sex, site, and IQ. See Additional file 1 for more details on stimuli, trial structure, and task instruction.
Across analyses, effects were evaluated at a statistical threshold of p = 0. 05, family-wise error corrected (FWE) on a voxel level across the whole brain and within the combined mask of the right pSTS and dmPFC using small volume correction (SVC; 503 voxels). Casecontrol analyses were complemented by tests for equality of distribution at selected peak voxels, using the Kolmogorov-Smirnov test with a significance level of α =. 05 implemented as the ks2stat function in Matlab (version R2018b, The MathWorks, Inc. ).
This association was only observed in the ASD group and predicted higher brain responses in individuals with higher autistic traits, irrespective of age. The lack of robust case-control differences needs to be discussed in the light of the positive findings by Castelli et al. [7]  and others using the animated shapes task  [12] [13] [14]. First, as discussed above in the context of non-replicable age effects, one possible explanation is that previously reported case-control differences in studies with low statistical power reflect idiosyncratic characteristics of the specific sample and experimental procedure. A recent study using simulated and empirical fMRI data provided compelling evidence that between-subject effects, such as casecontrol differences and brain-behavior relationships, are usually weak and spatially distributed  [44].
Small methodological differences to the original study by Castelli et al. [7]  might have contributed to the lack of case-control differences on the behavioral level (e. g. , use of 3-point instead of 6point scale for obtaining narrative scores). Overall, the pattern of strong within-subject effects and small-to-absent between-subject effects might partly result from substantial between-subject heterogeneity, which may be exacerbated in multicenter designs, but is a feature of samples that strive for representativeness for the underlying population. While this is the case for the current study, we followed recommended procedures to ensure optimal alignment of data acquisition between centers  [66]  and aimed for a deep multimodal characterization of our study participants  [24, 37]. Furthermore, our distributional observations do not support the interpretation of distinct subgroups of participants with ASD masking group mean effects.
Conclusions In the current report, we examined social brain correlates of mentalizing in ASD in a large and well-powered task-based fMRI dataset. Developmental effects were observed in younger individuals who showed a stronger modulation of attention-related brain areas by mentalizing demands. Against our expectation, we did not replicate previous observations of reduced activation in individuals with ASD. Instead, brain responses in autistic individuals were robustly typical in that they showed the expected effects of task in the social brain circuitry. We therefore conclude that time-locked functional activation in an animated shapes task does not inform the neurobiological basis of the mind-blindness account of ASD. This calls for the interrogation of different neural phenotypes, in particular connectivity and connectomic measures such as those derived from graph theory, which may access brain functional interaction not captured by the activation measures investigated here.
Acknowledgements We thank all participants and their families for their efforts to participate in the study. Group authorship:  Jumana Ahmad ,  Sara Ambrosino ,  Bonnie Auyeung ,  Tobias Banaschewski ,  Simon Baron-Cohen ,  Sarah Baumeister ,  Christian F. Beckmann ,  Sven Bölte ,  Thomas Bourgeron ,  Carsten Bours ,  Michael Brammer ,  Daniel Brandeis ,  Claudia Brogna ,  Yvette de Bruijn ,  Jan K. Buitelaar ,  Bhismadev Chakrabarti ,  Tony Charman ,  Ineke Cornelissen ,  Daisy Crawley ,  Flavio Dell'Acqua ,  Guillaume Dumas ,  Sarah Durston ,  Christine Ecker ,  Jessica Faulkner ,  Vincent Frouin ,  Pilar Garcés ,  David Goyard ,  Lindsay Ham ,  Hannah Hayward ,  Joerg Hipp ,  Rosemary Holt ,  Mark H. Johnson ,  Emily J. H. Jones ,  Prantik Kundu ,  Meng-Chuan Lai , Xavier Liogier D'ardhuy,  Michael V.
Funding This project has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  777394  for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS ,  Autistica, SFARI. Additional funding was received by the  European Community 's  Seventh Framework Programme  under the grant agreement no. 602805  (Project  EU-AGGRESSOTYPE ) and no. 602450  (Project  EU-IMAGEMEND ), and by the  German Federal Ministry of Education and Research  under the grant no. 01ZX1314GM  ( Project IntegraMent ) and grant no. 01GQ1102. C. M. is a recipient of an  Olympia-Morata  grant of the  University Heidelberg.
[9]  trained a model to identify such factors in full texts of cases. In a promising approach  [10]  Branting and colleagues trained a machine learning program called SCALE (semi-supervised case annotation for legal explanations) to label text excerpts in WIPO (World Intellectual Property Organization) domain name dispute cases by applicable legal issues and factors. The ultimate goal is for SCALE to employ the issue and factor labels in explaining its predictions of outcomes of new cases in terms of reasons that legal professionals would understand. Beyond connecting case sentences to reasons, issue and factor tags could connect the case to computational models of legal argument like those mentioned above. A text analytic program like SCALE could identify the applicable issues and factors, and a case-based model, equipped with a database of cases annotated by SCALE, could assist in explaining and testing its predictions.
Introduction Search is a basic function supported in all digital archives of information. While search techniques have evolved in structured and unstructured databases, it is still an ongoing research issue in digital libraries and document databases  [10] , in stark contrast with other types of digital libraries such as music  [6] , mathematics  [14] , judicial  [4] , etc. in terms of techniques and applications. Among the search techniques currently in use, some form of keyword search  [17]  or text mining based association search  [1]  are prevalent. In recent research, however, an emerging trend of searching digital libraries using knowledge graphs (KG) is gaining popularity  [8]  with the goal to improve semantic matching  [9]. It is not uncommon in digital library search for users to land on useful documents almost by accident  [13].
With respect to digital representation of judicial documents, counsels of the parties involved in a litigation submit legal briefs, courts rule on the briefs, and produce another document called ruling or judgment. These rulings become legal precedents and enter the database as case laws. A ruling has roughly four parts: 1) court, party, counsel details, and case details such as case number, dates and jurisdiction. 2) a preamble that states the overall description of the litigation and applicable case laws. 3) facts that lay out the "truth" about the case as seen by each party which can be established by evidence. 4) legal argument why or why not the facts lead to legal conclusions supported by case laws. Finally, 5) relief sought or final opinion of the courts after deliberations and argument.
Implementation of Prism as a Virtual Office Environment in the Cloud We have implemented Prism as a virtual legal office environment, called VOiC, in which document privacy and controlled sharing are top priorities. We have used Flask for its well-known support for web applications, using its two core components Werkzeug for web server functions and Jinja for HTML templating. Flask is extendable by virtue of its support for many extensions, and it also works with the majority of third-party Python libraries, which we have used as well. In addition to several other extensions of Flask, we have used the flask ckeditor extension. CKEditor is an embeddable rich text editor with full support for HTML editing. This extension allows a core feature of Prism-HTML editing and embedding of graphs directly into documents.
Bootstrap 4 open source frontend framework was used for creating platform-agnostic and responsive websites using its wide range of CSS styling options. For data management, SQLAlchemy was used to seamlessly convert data from a SQLite relational database into Python objects. VOiC provides a comprehensive document management system -a Virtual Office in the Cloud. It consists of four main processes: storing, sharing, searching, and rendering. Storage includes the holding of users, roles, and documents in a relation database. Sharing relates to access control, and allowing users to access pertinent documents through their username and role. Searching serves users with a tool for data discovery with documents searchable by their search graph, title, and content. Rendering forms the front-end portion of VOiC.
Related Work Previous work in legal contracts has focused on understanding tasks in legal NLP directed towards easier review and analysis of contracts. Identification of entities in contracts is looked upon in  [3] ,  [4] , the latter introducing a dataset of 179 contracts specific to lease agreements. The dataset introduced by  [4 ] also provides labeled data for identifying potentially unfair clauses, a task attempted prior by  [5]. [6]  identify critical contract clauses using a set of Context-Free Grammar (CFG) rules. The task of semantically retrieving legal clauses from a library of contracts as a span identification problem was introduced in  [7]. An expert annotation dataset for contract review is provided in  [8]. LEDGAR -a dataset for large-scale multilabel classification was introduced in  [1].
The sheer size of this dataset and the variety of clause types covered makes its applicability much beyond the task for which it was introduced, an example being the current work for clause generation. Since legal language is different from the general open domain language on which most of the models are pretrained, special attention has been paid to developing models to give representations specialized to the domain. LegalBERT  [9]  introduces a family of BERT-based models for application on downstream legal tasks. [2]  train a further pretrained version of BERT for use in context building for clause recommendation and show the superiority of the trained embeddings by showing distinguishable representations based on the clause type. ALeaseBERT trained in  [4]  is another task-specific pretrained model on legal contracts.
Experimentation This section explains our filtering and re-purposing of the LEDGAR dataset, experiments with encoder models, different values of k for similar contract retrieval, and metrics chosen for evaluation. Dataset. We re-purpose the LEDGAR  [1]  dataset for multilabel clause type identification for our task. The dataset in its cleaned version consists of 60,540 contracts having 846,274 clauses from 12,608 different types. We filter the dataset first to eliminate all contracts with less than five clauses present, resulting in 34,442 contracts, following which we select the top 15 clause types for analyzing the generation. The selected clause types, along with their clause counts and length information, can be seen in Table  1. Encoders. Considering the domain-specific nature of English in legal contracts, directly using transformer encoder models pretrained on generic corpora would not yield good results, as remarked in  [2].
Strategy Table  3  shows the metrics across all the strategies for the top 3 clause types (based on the no. of clauses in the dataset) and the overall scores computed for clauses for all the 15 clause types. Metrics for individual clause types have not been added due to space constraints. From the results, it can be easily observed that similar contracts with clause type and input contract provide the best contextual representation among the strategies tried. However, the strategy excluding similar contract representation performs competitively with the former. In a clause-type agnostic setting, the problem becomes much more difficult with modest ROUGE scores and dismal BLEU scores indicating a high amount of hallucinated, irrelevant content. Unlike clause type aware setting, the augmentation of similar contract information does not help, with an observed degradation in model output.
In recent years, there has been an increase in work aimed at performing the task of case prediction through the use of data-driven methods that learn from the large datasets now available. Key representative examples are the work presented in  [12] ,  [13]  and  [14]. Problems with these approaches include lack of accuracy (typically around 70-80%), degradation of performance as the training set ages, and lack of explanations. State-ofthe-art transformer-based models  [15]  for NLP tasks are unsuitable for long document classification, and proposed solutions  [16]  such as hierarchical transformer methods  [17]  rely on very large data sets and significant pre-training in order to produce decent results. None of these problems apply to symbolic models. Our motivation for the work set out in this paper is to bring together into a hybrid system benefits found within symbolic and data-driven approaches to legal case-based reasoning.
The main reason is the lack of a large-scale dataset and a method for end-to-end learning from input natural language to output logical expression. In the software industry, converting natural language to another form of software resource like database schema receives more attention than logical expressions. Therefore, it becomes easier to find a large-scale dataset for database schema  [15]. The second reason is that to be meaningful, the logical formula should be well defined in a formal language containing definitions and rules. Building such a language consumes a lot of effort in both research and development. Fortunately, PROLEG  [4]  provides a language for representing logical expression in the legal domain. With that condition, this work can be considered the first step to solving the problem of converting natural language to logical expressions automatically.
General Framework Solving the problem of translating natural language into a logical formula, we need to deal with two challenges. The first one is the limitation of training data. There is no available dataset that can be used to directly train a model for this task. Furthermore, the professional annotators who can provide high-quality data are also limited. The second challenge is that the logical syntax is much more rigid than the natural language. The translation should be very precise in order to get the correct logical formula. We find that this is an interesting and challenging problem that has not been well explored in the field of natural language processing and deep learning. We propose the general framework as in Figure  1. There are two main modules: translation and correction.
Output -Lessor("Sanna Mirella Marin"). -Lessee("LEITCH Michael"). -agreement of lease contract("Sanna Mirella Marin","LEITCH Michael","Room","Sunrise Village, Arkansas, 72207 United States","$5 per month","2018 year 10 month 15 day", , "the next business day","This Room Rental Agreement"). Table  1. Sample of data for translation module. Interestingly, its errors follow some patterns, which can be learned by another neural network module (the correction module). Data preparation for the correction module can be done in several automatic strategies with examples described in Table  2. We generate a dataset of 20,000 samples, which is much larger than the training set of the translation module.