pwc-archive/papers-with-abstracts huggingface dataset:
Compiling a Suitable Level of Sense Granularity in a Lexicon for AI Purposes: The Open Source COR Lexicon
Authors: Bolette Pedersen, Nathalie Carmen Hau Sørensen, Sanni Nimb, Ida Flørke, Sussi Olsen, Thomas Troelsgård
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

We present The Central Word Register for Danish (COR), which is an open source lexicon project for general AI purposes funded and initiated by the Danish Agency for Digitisation as part of an AI initiative embarked by the Danish Government in 2020. We focus here on the lexical semantic part of the project (COR-S) and describe how we – based on the existing fine-grained sense inventory from Den Danske Ordbog (DDO) – compile a more AI suitable sense granularity level of the vocabulary. A three-step methodology is applied: We establish a set of linguistic principles for defining core senses in COR-S and from there, we generate a hand-crafted gold standard of 6,000 lemmas depicting how to come from the fine-grained DDO sense to the COR inventory. Finally, we experiment with a number of language models in order to automatize the sense reduction of the rest of the lexicon. The models comprise a ruled-based model that applies our linguistic principles in terms of features, a word2vec model using cosine similarity to measure the sense proximity, and finally a deep neural BERT model fine-tuned on our annotations. The rule-based approach shows best results, in particular on adjectives, however, when focusing on the average polysemous vocabulary, the BERT model shows promising results too.
Sense and Sentiment
Authors: Francis Bond, Merrick Choo
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

In this paper we examine existing sentiment lexicons and sense-based sentiment-tagged corpora to find out how sense and concept-based semantic relations effect sentiment scores (for polarity and valence). We show that some relations are good predictors of sentiment of related words: antonyms have similar valence and opposite polarity, synonyms similar valence and polarity, as do many derivational relations. We use this knowledge and existing resources to build a sentiment annotated wordnet of English, and show how it can be used to produce sentiment lexicons for other languages using the Open Multilingual Wordnet.
Enriching Linguistic Representation in the Cantonese Wordnet and Building the New Cantonese Wordnet Corpus
Authors: Ut Seong Sio, Luís Morgado da Costa
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This paper reports on the most recent improvements on the Cantonese Wordnet, a wordnet project started in 2019 (Sio and Morgado da Costa, 2019) with the aim of capturing and organizing lexico-semantic information of Hong Kong Cantonese. The improvements we present here extend both the breadth and depth of the Cantonese Wordnet: increasing the general coverage, adding functional categories, enriching verbal representations, as well as creating the Cantonese Wordnet Corpus – a corpus of handcrafted examples where individual senses are shown in context.
ZAEBUC: An Annotated Arabic-English Bilingual Writer Corpus
Authors: Nizar Habash, David Palfreyman
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Lemmatization, Part-Of-Speech Tagging, POS, POS Tagging

We present ZAEBUC, an annotated Arabic-English bilingual writer corpus comprising short essays by first-year university students at Zayed University in the United Arab Emirates. We describe and discuss the various guidelines and pipeline processes we followed to create the annotations and quality check them. The annotations include spelling and grammar correction, morphological tokenization, Part-of-Speech tagging, lemmatization, and Common European Framework of Reference (CEFR) ratings. All of the annotations are done on Arabic and English texts using consistent guidelines as much as possible, with tracked alignments among the different annotations, and to the original raw texts. For morphological tokenization, POS tagging, and lemmatization, we use existing automatic annotation tools followed by manual correction. We also present various measurements and correlations with preliminary insights drawn from the data and annotations. The publicly available ZAEBUC corpus and its annotations are intended to be the stepping stones for additional annotations.
Turkish Universal Conceptual Cognitive Annotation
Authors: Necva Bölücü, Burcu Can
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Few-Shot Learning

Universal Conceptual Cognitive Annotation (UCCA) (Abend and Rappoport, 2013a) is a cross-lingual semantic annotation framework that provides an easy annotation without any requirement for linguistic background. UCCA-annotated datasets have been already released in English, French, and German. In this paper, we introduce the first UCCA-annotated Turkish dataset that currently involves 50 sentences obtained from the METU-Sabanci Turkish Treebank (Atalay et al., 2003; Oflazeret al., 2003). We followed a semi-automatic annotation approach, where an external semantic parser is utilised for an initial annotation of the dataset, which is partially accurate and requires refinement. We manually revised the annotations obtained from the semantic parser that are not in line with the UCCA rules that we defined for Turkish. We used the same external semantic parser for evaluation purposes and conducted experiments with both zero-shot and few-shot learning. While the parser cannot predict remote edges in zero-shot setting, using even a small subset of training data in few-shot setting increased the overall F-1 score including the remote edges. This is the initial version of the annotated dataset and we are currently extending the dataset. We will release the current Turkish UCCA annotation guideline along with the annotated dataset.
Introducing the CURLICAT Corpora: Seven-language Domain Specific Annotated Corpora from Curated Sources
Authors: Tamás Váradi, Bence Nyéki, Svetla Koeva, Marko Tadić, Vanja Štefanec, Maciej Ogrodniczuk, Bartłomiej Nitoń, Piotr Pęzik, Verginica Barbu Mititelu, Elena Irimia, Maria Mitrofan, Dan Tufiș, Radovan Garabík, Simon Krek, Andraž Repar
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: NMT

This article presents the current outcomes of the CURLICAT CEF Telecom project, which aims to collect and deeply annotate a set of large corpora from selected domains. The CURLICAT corpus includes 7 monolingual corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing selected samples from respective national corpora. These corpora are automatically tokenized, lemmatized and morphologically analysed and the named entities annotated. The annotations are uniformly provided for each language specific corpus while the common metadata schema is harmonised across the languages. Additionally, the corpora are annotated for IATE terms in all languages. The file format is CoNLL-U Plus format, containing the ten columns specific to the CoNLL-U format and three extra columns specific to our corpora as defined by Varádi et al. (2020). The CURLICAT corpora represent a rich and valuable source not just for training NMT models, but also for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.
RU-ADEPT: Russian Anonymized Dataset with Eight Personality Traits
Authors: C. Anton Rytting, Valerie Novak, James R. Hull, Victor M. Frank, Paul Rodrigues, Jarrett G. W. Lee, Laurel Miller-Sims
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

Social media has provided a platform for many individuals to easily express themselves naturally and publicly, and researchers have had the opportunity to utilize large quantities of this data to improve author trait analysis techniques and to improve author trait profiling systems. The majority of the work in this area, however, has been narrowly spent on English and other Western European languages, and generally focuses on a single social network at a time, despite the large quantity of data now available across languages and differences that have been found across platforms. This paper introduces RU-ADEPT, a dataset of Russian authors’ personality trait scores–Big Five and Dark Triad, demographic information (e.g. age, gender), with associated corpus of the authors’ cross-contributions to (up to) four different social media platforms–VKontakte (VK), LiveJournal, Blogger, and Moi Mir. We believe this to be the first publicly-available dataset associating demographic and personality trait data with Russian-language social media content, the first paper to describe the collection of Dark Triad scores with texts across multiple Russian-language social media platforms, and to a limited extent, the first publicly-available dataset of personality traits to author content across several different social media sites.
User Interest Modelling in Argumentative Dialogue Systems
Authors: Annalena Aicher, Nadine Gerstenlauer, Wolfgang Minker, Stefan Ultes
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

Most systems helping to provide structured information and support opinion building, discuss with users without considering their individual interest. The scarce existing research on user interest in dialogue systems depends on explicit user feedback. Such systems require user responses that are not content-related and thus, tend to disturb the dialogue flow. In this paper, we present a novel model for implicitly estimating user interest during argumentative dialogues based on semantically clustered data. Therefore, an online user study was conducted to acquire training data which was used to train a binary neural network classifier in order to predict whether or not users are still interested in the content of the ongoing dialogue. We achieved a classification accuracy of 74.9% and furthermore investigated with different Artificial Neural Networks (ANN) which new argument would fit the user interest best.
Every time I fire a conversational designer, the performance of the dialogue system goes down
Authors: Giancarlo Xompero, Michele Mastromattei, Samir Salman, Cristina Giannone, Andrea Favalli, Raniero Romagnoli, Fabio Massimo Zanzotto
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Task-Oriented Dialogue Systems

Incorporating handwritten domain scripts into neural-based task-oriented dialogue systems may be an effective way to reduce the need for large sets of annotated dialogues. In this paper, we investigate how the use of domain scripts written by conversational designers affects the performance of neural-based dialogue systems. To support this investigation, we propose the Conversational-Logic-Injection-in-Neural-Network system (CLINN) where domain scripts are coded in semi-logical rules. By using CLINN, we evaluated semi-logical rules produced by a team of differently-skilled conversational designers. We experimented with the Restaurant domain of the MultiWOZ dataset. Results show that external knowledge is extremely important for reducing the need for annotated examples for conversational systems. In fact, rules from conversational designers used in CLINN significantly outperform a state-of-the-art neural-based dialogue system when trained with smaller sets of annotated dialogues.
Language Technologies for the Creation of Multilingual Terminologies. Lessons Learned from the SSHOC Project
Authors: Federica Gamba, Francesca Frontini, Daan Broeder, Monica Monachini
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Machine Translation, Translation, valid

This paper is framed in the context of the SSHOC project and aims at exploring how Language Technologies can help in promoting and facilitating multilingualism in the Social Sciences and Humanities (SSH). Although most SSH researchers produce culturally and societally relevant work in their local languages, metadata and vocabularies used in the SSH domain to describe and index research data are currently mostly in English. We thus investigate Natural Language Processing and Machine Translation approaches in view of providing resources and tools to foster multilingual access and discovery to SSH content across different languages. As case studies, we create and deliver as freely, openly available data a set of multilingual metadata concepts and an automatically extracted multilingual Data Stewardship terminology. The two case studies allow as well to evaluate performances of state-of-the-art tools and to derive a set of recommendations as to how best apply them. Although not adapted to the specific domain, the employed tools prove to be a valid asset to translation tasks. Nonetheless, validation of results by domain experts proficient in the language is an unavoidable phase of the whole workflow.
How to be FAIR when you CARE: The DGS Corpus as a Case Study of Open Science Resources for Minority Languages
Authors: Marc Schulder, Thomas Hanke
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

The publication of resources for minority languages requires a balance between making data open and accessible and respecting the rights and needs of its language community. The FAIR principles were introduced as a guide to good open data practices and they have since been complemented by the CARE principles for indigenous data governance. This article describes how the DGS Corpus implemented these principles and how the two sets of principles affected each other. The DGS Corpus is a large collection of recordings of members of the deaf community in Germany communicating in their primary language, German Sign Language (DGS); it was created to be both as a resource for linguistic research and as a record of the life experiences of deaf people in Germany. The corpus was designed with CARE in mind to respect and empower the language community and FAIR data publishing was used to enhance its usefulness as a scientific resource.
Italian NLP for Everyone: Resources and Models from EVALITA to the European Language Grid
Authors: Valerio Basile, Cristina Bosco, Michael Fell, Viviana Patti, Rossella Varvara
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

The European Language Grid enables researchers and practitioners to easily distribute and use NLP resources and models, such as corpora and classifiers. We describe in this paper how, during the course of our EVALITA4ELG project, we have integrated datasets and systems for the Italian language. We show how easy it is to use the integrated systems, and demonstrate in case studies how seamless the application of the platform is, providing Italian NLP for everyone.
Cross-Lingual Link Discovery for Under-Resourced Languages
Authors: Michael Rosner, Sina Ahmadi, Elena-Simona Apostol, Julia Bosque-Gil, Christian Chiarcos, Milan Dojchinovski, Katerina Gkirtzou, Jorge Gracia, Dagmar Gromann, Chaya Liebeskind, Giedrė Valūnaitė Oleškevičienė, Gilles Sérasset, Ciprian-Octavian Truică
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

In this paper, we provide an overview of current technologies for cross-lingual link discovery, and we discuss challenges, experiences and prospects of their application to under-resourced languages. We rst introduce the goals of cross-lingual linking and associated technologies, and in particular, the role that the Linked Data paradigm (Bizer et al., 2011) applied to language data can play in this context. We de ne under-resourced languages with a speci c focus on languages actively used on the internet, i.e., languages with a digitally versatile speaker community, but limited support in terms of language technology. We argue that languages for which considerable amounts of textual data and (at least) a bilingual word list are available, techniques for cross-lingual linking can be readily applied, and that these enable the implementation of downstream applications for under-resourced languages via the localisation and adaptation of existing technologies and resources.
Angry or Sad ? Emotion Annotation for Extremist Content Characterisation
Authors: Valentina Dragos, Delphine Battistelli, Aline Etienne, Yolène Constable
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This paper examines the role of emotion annotations to characterize extremist content released on social platforms. The analysis of extremist content is important to identify user emotions towards some extremist ideas and to highlight the root cause of where emotions and extremist attitudes merge together. To address these issues our methodology combines knowledge from sociological and linguistic annotations to explore French extremist content collected online. For emotion linguistic analysis, the solution presented in this paper relies on a complex linguistic annotation scheme. The scheme was used to annotate extremist text corpora in French. Data sets were collected online by following semi-automatic procedures for content selection and validation. The paper describes the integrated annotation scheme, the annotation protocol that was set-up for French corpora annotation and the results, e.g. agreement measures and remarks on annotation disagreements. The aim of this work is twofold: first, to provide a characterization of extremist contents; second, to validate the annotation scheme and to test its capacity to capture and describe various aspects of emotions.
Identification of Multiword Expressions in Tweets for Hate Speech Detection
Authors: Nicolas Zampieri, Carlos Ramisch, Irina Illina, Dominique Fohr
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Hate Speech Detection

Multiword expression (MWE) identification in tweets is a complex task due to the complex linguistic nature of MWEs combined with the non-standard language use in social networks. MWE features were shown to be helpful for hate speech detection (HSD). In this article, we present joint experiments on these two related tasks on English Twitter data: first we focus on the MWE identification task, and then we observe the influence of MWE-based features on the HSD task. For MWE identification, we compare the performance of two systems: lexicon-based and deep neural networks-based (DNN). We experimentally evaluate seven configurations of a state-of-the-art DNN system based on recurrent networks using pre-trained contextual embeddings from BERT. The DNN-based system outperforms the lexicon-based one thanks to its superior generalisation power, yielding much better recall. For the HSD task, we propose a new DNN architecture for incorporating MWE features. We confirm that MWE features are helpful for the HSD task. Moreover, the proposed DNN architecture beats previous MWE-based HSD systems by 0.4 to 1.1 F-measure points on average on four Twitter HSD corpora.
Causal Investigation of Public Opinion during the COVID-19 Pandemic via Social Media Text
Authors: Michael Jantscher, Roman Kern
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Causal Inference, Sentiment Analysis, valid

Understanding the needs and fears of citizens, especially during a pandemic such as COVID-19, is essential for any government or legislative entity. An effective COVID-19 strategy further requires that the public understand and accept the restriction plans imposed by these entities. In this paper, we explore a causal mediation scenario in which we want to emphasize the use of NLP methods in combination with methods from economics and social sciences. Based on sentiment analysis of Tweets towards the current COVID-19 situation in the UK and Sweden, we conduct several causal inference experiments and attempt to decouple the effect of government restrictions on mobility behavior from the effect that occurs due to public perception of the COVID-19 strategy in a country. To avoid biased results we control for valid country specific epidemiological and time-varying confounders. Comprehensive experiments show that not all changes in mobility are caused by countries implemented policies but also by the support of individuals in the fight against this pandemic. We find that social media texts are an important source to capture citizens’ concerns and trust in policy makers and are suitable to evaluate the success of government policies.
Automatic Detection of Stigmatizing Uses of Psychiatric Terms on Twitter
Authors: Véronique Moriceau, Farah Benamara, Abdelmoumene Boumadane
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

Psychiatry and people suffering from mental disorders have often been given a pejorative label that induces social rejection.Many studies have addressed discourse content about psychiatry on social media, suggesting that they convey stigmatizingrepresentations of mental health disorders. In this paper, we focus for the first time on the use of psychiatric terms in tweetsin French. We first describe the annotated dataset that we use. Then we propose several deep learning models to detectautomatically (1) the different types of use of psychiatric terms (medical use, misuse or irrelevant use), and (2) the polarityof the tweet. We show that polarity detection can be improved when done in a multitask framework in combination with typeof use detection. This confirms the observations made manually on several datasets, namely that the polarity of a tweet iscorrelated to the type of term use (misuses are mostly negative whereas medical uses are neutral). The results are interesting forboth tasks and it allows to consider the possibility for performant automatic approaches in order to conduct real-time surveyson social media, larger and less expensive than existing manual ones
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models
Authors: Areej Alhassan, Jinkai Zhang, Viktor Schlegel
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Anomaly Detection, Natural Language Inference

Natural language processing (NLP) has been shown to perform well in various tasks, such as answering questions, ascertaining natural language inference and anomaly detection. However, there are few NLP-related studies that touch upon the moral context conveyed in text. This paper studies whether state-of-the-art, pre-trained language models are capable of passing moral judgments on posts retrieved from a popular Reddit user board. Reddit is a social discussion website and forum where posts are promoted by users through a voting system. In this work, we construct a dataset that can be used for moral judgement tasks by collecting data from the AITA? (Am I the A*******?) subreddit. To model our task, we harnessed the power of pre-trained language models, including BERT, RoBERTa, RoBERTa-large, ALBERT and Longformer. We then fine-tuned these models and evaluated their ability to predict the correct verdict as judged by users for each post in the datasets. RoBERTa showed relative improvements across the three datasets, exhibiting a rate of 87% accuracy and a Matthews correlation coefficient (MCC) of 0.76, while the use of the Longformer model slightly improved the performance when used with longer sequences, achieving 87% accuracy and 0.77 MCC.
Generating Questions from Wikidata Triples
Authors: Kelvin Han, Thiago castro Ferreira, Claire Gardent
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Knowledge Base Question Answering, Question Answering, Question Generation, Question-Generation

Question generation from knowledge bases (or knowledge base question generation, KBQG) is the task of generating questions from structured database information, typically in the form of triples representing facts. To handle rare entities and generalize to unseen properties, previous work on KBQG resorted to extensive, often ad-hoc pre- and post-processing of the input triple. We revisit KBQG – using pre training, a new (triple, question) dataset and taking question type into account – and show that our approach outperforms previous work both in a standard and in a zero-shot setting. We also show that the extended KBQG dataset (also helpful for knowledge base question answering) we provide allows not only for better coverage in terms of knowledge base (KB) properties but also for increased output variability in that it permits the generation of multiple questions from the same KB triple.