pwc-archive/papers-with-abstracts huggingface dataset:
Categorizing legal features in a metadata-oriented task: defining the conditions of use
Authors: Mickaël Rigault, Victoria Arranz, Valérie Mapelli, Penny Labropoulou, Stelios Piperidis
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6

In recent times, more attention has been brought by the Human Language Technology (HLT) community to the legal framework for making available and reusing Language Resources (LR) and tools. Licensing is now an issue that is foreseen in most research projects and that is essential to provide legal certainty for repositories when distributing resources. Some repositories such as Zenodo or Quantum Stat do not offer the possibility to search for resources by licenses which can turn the searching for relevant resources a very complex task. Other repositories such as Hugging Face propose a search feature by license which may make it difficult to figure out what use can be made of such resources. During the European Language Grid (ELG) project, we moved a step forward to link metadata with the terms and conditions of use. In this paper, we document the process we undertook to categorize legal features of licenses listed in the SPDX license list and widely used in the HLT community as well as those licenses used within the ELG platform
About Migration Flows and Sentiment Analysis on Twitter data: Building the Bridge between Technical and Legal Approaches to Data Protection
Authors: Thilo Gottschalk, Francesca Pichierri
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6
Tasks: Sentiment Analysis

Sentiment analysis has always been an important driver of political decisions and campaigns across all fields. Novel technologies allow automatizing analysis of sentiments on a big scale and hence provide allegedly more accurate outcomes. With user numbers in the billions and their increasingly important role in societal discussions, social media platforms become a glaring data source for these types of analysis. Due to its public availability, the relative ease of access and the sheer amount of available data, the Twitter API has become a particularly important source to researchers and data analysts alike. Despite the evident value of these data sources, the analysis of such data comes with legal, ethical and societal risks that should be taken into consideration when analysing data from Twitter. This paper describes these risks along the technical processing pipeline and proposes related mitigation measures.
Transparency and Explainability of a Machine Learning Model in the Context of Human Resource Management
Authors: Sebastien Delecraz, Loukman Eltarr, Olivier Oullier
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6
Tasks: Fairness, Management

We introduce how the proprietary machine learning algorithms developed by Gojob, an HR Tech company, to match candidates to a job offer are as transparent and explainable as possible to users (i.e., our recruiters) and our clients (e.g. companies looking to fill jobs). We detail how our matching algorithm (which identifies the best candidates for a job offer) controls the fairness of its outcome. We have described the steps we have taken to ensure that the decisions made by our mathematical models not only inform but improve the performance of our recruiters.
Public Interactions with Voice Assistant – Discussion of Different One-Shot Solutions to Preserve Speaker Privacy
Authors: Ingo Siegert, Yamini Sinha, Gino Winkelmann, Oliver Jokisch, Andreas Wendemuth
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6

In recent years, the use of voice assistants has rapidly grown. Hereby, above all, the user’s speech data is stored and processed on a cloud platform, being the decisive factor for a good performance in speech processing and understanding. Although usually, they can be found in private households, a lot of business cases are also employed using voice assistants for public places, be it as an information service, a tour guide, or a booking system. As long as the systems are used in private spaces, it could be argued that the usage is voluntary and that the user itself is responsible for what is processed by the voice assistant system. When leaving the private space, the voluntary use is not the case anymore, as users may be made aware that their voice is processed in the cloud and background voices can be unintendedly recorded and processed as well. Thus, the usage of voice assistants in public environments raises a lot of privacy concerns. In this contribution, we discuss possible anonymization solutions to hide the speakers’ identity, thus allowing a safe cloud processing of speech data. Thereby, we promote the public use of voice assistants.
Keynote Speech - Voice anonymization and the GDPR
Authors: Brij Mohan Lal Srivastava
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6

Talk for the Workshop on Legal and Ethical Issues in Human Language Technologies, LREC 2022, Marseille, 24 June 2022
Cross-Clinic De-Identification of Swedish Electronic Health Records: Nuances and Caveats
Authors: Olle Bridal, Thomas Vakili, Marina Santini
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6
Tasks: De-identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Privacy preservation of sensitive information is one of the main concerns in clinical text mining. Due to the inherent privacy risks of handling clinical data, the clinical corpora used to create the clinical Named Entity Recognition (NER) models underlying clinical de-identification systems cannot be shared. This situation implies that clinical NER models are trained and tested on data originating from the same institution since it is rarely possible to evaluate them on data belonging to a different organization. These restrictions on sharing make it very difficult to assess whether a clinical NER model has overfitted the data or if it has learned any undetected biases. This paper presents the results of the first-ever cross-institution evaluation of a Swedish de-identification system on Swedish clinical data. Alongside the encouraging results, we discuss differences and similarities across EHR naming conventions and NER tagsets.
Generating Realistic Synthetic Curricula Vitae for Machine Learning Applications under Differential Privacy
Authors: Andrea Bruera, Francesco Alda, Francesco Di Cerbo
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6
Tasks: Management

Applications involving machine learning in Human Resources (HR, the management of human talent in order to accomplish organizational goals) must respect the privacy of the individuals whose data is being used. This is a difficult aim, given the extremely personal nature of text data handled by HR departments, such as Curricula Vitae (CVs).
MAPA Project: Ready-to-Go Open-Source Datasets and Deep Learning Technology to Remove Identifying Information from Text Documents
Authors: Victoria Arranz, Khalid Choukri, Montse Cuadros, Aitor García Pablos, Lucie Gianola, Cyril Grouin, Manuel Herranz, Patrick Paroubek, Pierre Zweigenbaum
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6
Tasks: De-identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper presents the outcomes of the MAPA project, a set of annotated corpora for 24 languages of the European Union and an open-source customisable toolkit able to detect and substitute sensitive information in text documents from any domain, using state-of-the art, deep learning-based named entity recognition techniques. In the context of the project, the toolkit has been developed and tested on administrative, legal and medical documents, obtaining state-of-the-art results. As a result of the project, 24 dataset packages have been released and the de-identification toolkit is available as open source.
PriPA: A Tool for Privacy-Preserving Analytics of Linguistic Data
Authors: Jeremie Clos, Emma McClaughlin, Pepita Barnard, Elena Nichele, Dawn Knight, Derek McAuley, Svenja Adolphs
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6
Tasks: Privacy Preserving

The days of large amorphous corpora collected with armies of Web crawlers and stored indefinitely are, or should be, coming to an end. There is a wealth of hidden linguistic information that is increasingly difficult to access, hidden in personal data that would be unethical and technically challenging to collect using traditional methods such as Web crawling and mass surveillance of online discussion spaces. Advances in privacy regulations such as GDPR and changes in the public perception of privacy bring into question the problematic ethical dimension of extracting information from unaware if not unwilling participants. Modern corpora need to adapt, be focused on testing specific hypotheses, and be respectful of the privacy of the people who generated its data. Our work focuses on using a distributed participatory approach and continuous informed consent to solve these issues, by allowing participants to voluntarily contribute their own censored personal data at a granular level. We evaluate our approach in a three-pronged manner, testing the accuracy of measurement of statistical measures of language with respect to standard corpus linguistics tools, evaluating the usability of our application with a participant involvement panel, and using the tool for a case study on health communication.
Legal and Ethical Challenges in Recording Air Traffic Control Speech
Authors: Mickaël Rigault, Claudia Cevenini, Khalid Choukri, Martin Kocour, Karel Veselý, Igor Szoke, Petr Motlicek, Juan Pablo Zuluaga-Gomez, Alexander Blatt, Dietrich Klakow, Allan Tart, Pavel Kolčárek, Jan Černocký
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6
Tasks: Ethics

In this paper the authors detail the various legal and ethical issues faced during the ATCO2 project. This project is aimed at developing tools to automatically collect and transcribe air traffic conversations, especially conversations between pilots and air controls towers. In this paper the authors will develop issues related to intellectual property, public data, privacy, and general ethics issues related to the collection of air-traffic control speech.
It is not Dance, is Data: Gearing Ethical Circulation of Intangible Cultural Heritage practices in the Digital Space
Authors: Jorge Yánez, Amel Fraisse
Date: 2022-06-01 00:00:00
Proceeding: LEGAL (LREC) 2022 6

The documentation, protection and dissemination of Intangible Cultural Heritage (ICH) in the digital age pose significant theoretical, technological and legal challenges. Through a multidisciplinary lens, this paper presents new approaches for collecting, documenting, encrypting and protecting ICH-related data for more ethical circulation. Human-movement recognition technologies such as motion capture, allows for the recording, extraction and reproduction of human movement with unprecedented precision. The once indistinguishable or hard-to-trace reproduction of dance steps between their creators and unauthorized third parties becomes patent through the transmission of embodied knowledge, but in the form of data. This new battlefield prompted by digital technologies only adds to the disputes within the creative industries, in terms of authorship, ownership and commodification of body language. For the sake of this paper, we are aiming to disentangle the various layers present in the process of digitisation of the dancing body, to identify its by-products as well as the possible arising ownership rights that might entail. ”Who owns what?”, the basic premise of intellectual property law, is transposed, in this case, onto the various types of data generated when intangible cultural heritage, in the form of dance, is digitised through motion capture and encrypted with blockchain technologies.
Towards the Profiling of Linked Lexicographic Resources
Authors: Lenka Bajcetic, Seung-bin Yim, Thierry Declerck
Date: 2022-06-01 00:00:00
Proceeding: gwll (LREC) 2022 6

This paper presents Edie: ELEXIS DIctionary Evaluator. Edie is designed to create profiles for lexicographic resources accessible through the ELEXIS platform. These profiles can be used to evaluate and compare lexicographic resources, and in particular they can be used to identify potential data that could be linked.
Towards the Linking of a Sign Language Ontology with Lexical Data
Authors: Thierry Declerck
Date: 2022-06-01 00:00:00
Proceeding: gwll (LREC) 2022 6

We describe our current work for linking a new ontology for representing constitutive elements of Sign Languages with lexical data encoded within the OntoLex-Lemon framework. We first present very briefly the current state of the ontology, and show how transcriptions of signs can be represented in OntoLex-Lemon, in a minimalist manner, before addressing the challenges of linking the elements of the ontology to full lexical descriptions of the spoken languages.
Modelling Collocations in OntoLex-FrAC
Authors: Christian Chiarcos, Katerina Gkirtzou, Maxim Ionov, Besim Kabashi, Fahad Khan, Ciprian-Octavian Truică
Date: 2022-06-01 00:00:00
Proceeding: gwll (LREC) 2022 6

Following presentations of frequency and attestations, and embeddings and distributional similarity, this paper introduces the third cornerstone of the emerging OntoLex module for Frequency, Attestation and Corpus-based Information, OntoLex-FrAC. We provide an RDF vocabulary for collocations, established as a consensus over contributions from five different institutions and numerous data sets, with the goal of eliciting feedback from reviewers, workshop audience and the scientific community in preparation of the final consolidation of the OntoLex-FrAC module, whose publication as a W3C community report is foreseen for the end of this year. The novel collocation component of OntoLex-FrAC is described in application to a lexicographic resource and corpus-based collocation scores available from the web, and finally, we demonstrate the capability and genericity of the model by showing how to retrieve and aggregate collocation information by means of SPARQL, and its export to a tabular format, so that it can be easily processed in downstream applications.
TIAD 2022: The Fifth Translation Inference Across Dictionaries Shared Task
Authors: Jorge Gracia, Besim Kabashi, Ilan Kernerman
Date: 2022-06-01 00:00:00
Proceeding: gwll (LREC) 2022 6
Tasks: Translation

The objective of the Translation Inference Across Dictionaries (TIAD) series of shared tasks is to explore and compare methods and techniques that infer translations indirectly between language pairs, based on other bilingual/multilingual lexicographic resources. In this fifth edition, the participating systems were asked to generate new translations automatically among three languages - English, French, Portuguese - based on known indirect translations contained in the Apertium RDF graph. Such evaluation pairs have been the same during the four last TIAD editions. Since the fourth edition, however, a larger graph is used as a basis to produce the translations, namely Apertium RDF v2. The evaluation of the results was carried out by the organisers against manually compiled language pairs of K Dictionaries. For the second time in the TIAD series, some systems beat the proposed baselines. This paper gives an overall description of the shard task, the evaluation data and methodology, and the systems’ results.
Creating Bilingual Dictionaries from Existing Ones by Means of Pivot-Oriented Translation Inference and Logistic Regression
Authors: Yves Bestgen
Date: 2022-06-01 00:00:00
Proceeding: gwll (LREC) 2022 6
Tasks: Translation

To produce new bilingual dictionaries from existing ones, an important task in the field of translation, a system based on a very classical supervised learning technique, with no other knowledge than the available bilingual dictionaries, is proposed. It performed very well in the Translation Inference Across Dictionaries (TIAD) shared task on the combined 2021 and 2022 editions. An analysis of the pros and cons suggests a series of avenues to further improve its effectiveness.
Compiling a Highly Accurate Bilingual Lexicon by Combining Different Approaches
Authors: Steinþór Steingrímsson, Luke O’Brien, Finnur Ingimundarson, Hrafn Loftsson, Andy Way
Date: 2022-06-01 00:00:00
Proceeding: gwll (LREC) 2022 6
Tasks: Cross-Lingual Word Embeddings, Machine Translation, Word Embeddings

Bilingual lexicons can be generated automatically using a wide variety of approaches. We perform a rigorous manual evaluation of four different methods: word alignments on different types of bilingual data, pivoting, machine translation and cross-lingual word embeddings. We investigate how the different setups perform using publicly available data for the English-Icelandic language pair, doing separate evaluations for each method, dataset and confidence class where it can be calculated. The results are validated by human experts, working with a random sample from all our experiments. By combining the most promising approaches and data sets, using confidence scores calculated from the data and the results of manually evaluating samples from our manual evaluation as indicators, we are able to induce lists of translations with a very high acceptance rate. We show how multiple different combinations generate lists with well over 90% acceptance rate, substantially exceeding the results for each individual approach, while still generating reasonably large candidate lists. All manually evaluated equivalence pairs are published in a new lexicon of over 232,000 pairs under an open license.
Converting a Database of Complex German Word Formation for Linked Data
Authors: Petra Steiner
Date: 2022-06-01 00:00:00
Proceeding: gwll (LREC) 2022 6

This work combines two lexical resources with morphological information on German word formation, CELEX for German and the latest release of GermaNet, for extracting and building complex word structures. This yields a database of over 100,000 German wordtrees. A definition for sequential morphological analyses leads to a Ontolex-Lemon type model. By using GermaNet sense information, the data can be linked to other semantic resources. An alignment to the CIDOC Conceptual Reference Model (CIDOC-CRM) is also provided. The scripts for the data generation are publicly available on GitHub.
Resolving Inflectional Ambiguity of Macedonian Adjectives
Authors: Katerina Zdravkova
Date: 2022-06-01 00:00:00
Proceeding: gwll (LREC) 2022 6

Macedonian adjectives are inflected for gender, number, definiteness and degree, with in average 47.98 inflections per headword. The inflection paradigm of qualificative adjectives is even richer, embracing 56.27 morphophonemic alterations. Depending on the word they were derived from, more than 600 Macedonian adjectives have an identical headword and two different word forms for each grammatical category. While non-verbal adjectives alter the root before adding the inflectional suffixes, suffixes of verbal adjectives are added directly to the root. In parallel with the morphological differences, both types of adjectives have a different translation, depending on the category of the words they have been derived from. Nouns that collocate with these adjectives are mutually disjunctive, enabling the resolution of inflectional ambiguity. They are organised as a lexical taxonomy, created using hierarchical divisive clustering. If embedded in the future spell-checking applications, this taxonomy will significantly reduce the risk of forming incorrect inflections, which frequently occur in the daily news and more often in the advertisements and social media.
Morpholex Turkish: A Morphological Lexicon for Turkish
Authors: Bilge Arican, Aslı Kuzgun, Büşra Marşan, Deniz Baran Aslan, Ezgi Sanıyar, Neslihan Cesur, Neslihan Kara, Oguzhan Kuyrukcu, Merve Ozcelik, Arife Betul Yenice, Merve Dogan, Ceren Oksal, Gökhan Ercan, Olcay Taner Yildiz
Date: 2022-06-01 00:00:00
Proceeding: gwll (LREC) 2022 6

MorphoLex is a study in which root, prefix and suffixes of words are analyzed. With MorphoLex, many words can be analyzed according to certain rules and a useful database can be created. Due to the fact that Turkish is an agglutinative language and the richness of its language structure, it offers different analyzes and results from previous studies in MorphoLex. In this study, we revealed the process of creating a database with 48,472 words and the results of the differences in language structure.
Time Travel in Turkish: WordNets for Modern Turkish
Authors: Ceren Oksal, Hikmet N. Oguz, Mert Catal, Nurkay Erbay, Ozgecan Yuzer, Ipek B. Unsal, Oguzhan Kuyrukcu, Arife B. Yenice, Aslı Kuzgun, Büşra Marşan, Ezgi Sanıyar, Bilge Arican, Merve Dogan, Özge Bakay, Olcay Taner Yildiz
Date: 2022-06-01 00:00:00
Proceeding: gwll (LREC) 2022 6

Wordnets have been popular tools for providing and representing semantic and lexical relations of languages. They are useful tools for various purposes in NLP studies. Many researches created WordNets for different languages. For Turkish, there are two WordNets, namely the Turkish WordNet of BalkaNet and KeNet. In this paper, we present new WordNets for Turkish each of which is based on one of the first 9 editions of the Turkish dictionary starting from the 1944 edition. These WordNets are historical in nature and make implications for Modern Turkish. They are developed by extending KeNet, which was created based on the 2005 and 2011 editions of the Turkish dictionary. In this paper, we explain the steps in creating these 9 new WordNets for Turkish, discuss the challenges in the process and report comparative results about the WordNets.
WordNet and Wikipedia Connection in Turkish WordNet KeNet
Authors: Merve Doğan, Ceren Oksal, Arife Betül Yenice, Fatih Beyhan, Reyyan Yeniterzi, Olcay Taner Yildiz
Date: 2022-06-01 00:00:00
Proceeding: gwll (LREC) 2022 6

This paper aims to present WordNet and Wikipedia connection by linking synsets from Turkish WordNet KeNet with Wikipedia and thus, provide a better machine-readable dictionary to create an NLP model with rich data. For this purpose, manual mapping between two resources is realized and 11,478 synsets are linked to Wikipedia. In addition to this, automatic linking approaches are utilized to analyze possible connection suggestions. Baseline Approach and ElasticSearch Based Approach help identify the potential human annotation errors and analyze the effectiveness of these approaches in linking. Adopting both manual and automatic mapping provides us with an encompassing resource of WordNet and Wikipedia connections.
ParlaMint II: The Show Must Go On
Authors: Maciej Ogrodniczuk, Petya Osenova, Tomaž Erjavec, Darja Fišer, Nikola Ljubešić, Çağrı Çöltekin, Matyáš Kopp, Meden Katja
Date: 2022-06-01 00:00:00
Proceeding: ParlaCLARIN (LREC) 2022 6

In ParlaMint I, a CLARIN-ERIC supported project in pandemic times, a set of comparable and uniformly annotated multilingual corpora for 17 national parliaments were developed and released in 2021. For 2022 and 2023, the project has been extended to ParlaMint II, again with the CLARIN ERIC financial support, in order to enhance the existing corpora with new data and metadata; upgrade the XML schema; add corpora for 10 new parliaments; provide more application scenarios and carry out additional experiments. The paper reports on these planned steps, including some that have already been taken, and outlines future plans.