pwc-archive/papers-with-abstracts huggingface dataset:
Writing System and Speaker Metadata for 2,800+ Language Varieties
Authors: Daan van Esch, Tamar Lucassen, Sebastian Ruder, Isaac Caswell, Clara Rivera
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

We describe an open-source dataset providing metadata for about 2,800 language varieties used in the world today. Specifically, the dataset provides the attested writing system(s) for each of these 2,800+ varieties, as well as an estimated speaker count for each variety. This dataset was developed through internal research and has been used for analyses around language technologies. This is the largest publicly-available, machine-readable resource with writing system and speaker information for the world’s languages. We analyze the distribution of languages and writing systems in our data and compare it to their representation in current NLP. We hope the availability of this data will catalyze research in under-represented languages.
The PALMA Corpora of African Varieties of Portuguese
Authors: Tjerk Hagemeijer, Amália Mendes, Rita Gonçalves, Catarina Cornejo, Raquel Madureira, Michel Généreux
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: LEMMA, POS

We present three new corpora of urban varieties of Portuguese spoken in Angola, Mozambique, and São Tomé and Príncipe, where Portuguese is increasingly being spoken as first and second language in different multilingual settings. Given the scarcity of linguistic resources available for the African varieties of Portuguese, these corpora provide new, contemporary data for the study of each variety and for comparative research on African, Brazilian and European varieties, hereby improving our understanding of processes of language variation and change in postcolonial societies. The corpora consist of transcribed spoken data, complemented by a rich set of metadata describing the setting of the audio recordings and sociolinguistic information about the speakers. They are annotated with POS and lemma information and made available on the CQPweb platform, which allows for sophisticated data searches. The corpora are already being used for comparative research on constructions in the domain of possession and location involving the argument structure of intransitive, monotransitive and ditransitive verbs that select Goals, Locatives, and Recipients.
A Learning-Based Dependency to Constituency Conversion Algorithm for the Turkish Language
Authors: Büşra Marşan, Oğuz K. Yıldız, Aslı Kuzgun, Neslihan Cesur, Arife B. Yenice, Ezgi Sanıyar, Oğuzhan Kuyrukçu, Bilge N. Arıcan, Olcay Taner Yildiz
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This study aims to create the very first dependency-to-constituency conversion algorithm optimised for Turkish language. For this purpose, a state-of-the-art morphologic analyser and a feature-based machine learning model was used. In order to enhance the performance of the conversion algorithm, bootstrap aggregating meta-algorithm was integrated. While creating the conversation algorithm, typological properties of Turkish were carefully considered. A comprehensive and manually annotated UD-style dependency treebank was the input, and constituency trees were the output of the conversion algorithm. A team of linguists manually annotated a set of constituency trees. These manually annotated trees were used as the gold standard to assess the performance of the algorithm. The conversion process yielded more than 8000 constituency trees whose UD-style dependency trees are also available on GitHub. In addition to its contribution to Turkish treebank resources, this study also offers a viable and easy-to-implement conversion algorithm that can be used to generate new constituency treebanks and training data for NLP resources like constituency parsers.
Standard German Subtitling of Swiss German TV content: the PASSAGE Project
Authors: Jonathan David Mutal, Pierrette Bouillon, Johanna Gerlach, Veronika Haberkorn
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: speech-recognition, Speech Recognition, Translation

In Switzerland, two thirds of the population speak Swiss German, a primarily spoken language with no standardised written form. It is widely used on Swiss TV, for example in news reports, interviews or talk shows, and subtitles are required for people who cannot understand this spoken language. This paper focuses on the task of automatic Standard German subtitling of spoken Swiss German, and more specifically on the translation of a normalised Swiss German speech recognition result into Standard German suitable for subtitles. Our contribution consists of a comparison of different statistical and deep learning MT systems for this task and an aligned corpus of normalised Swiss German and Standard German subtitles. Results of two evaluations, automatic and human, show that the systems succeed in improving the content, but are currently not capable of producing entirely correct Standard German.
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish
Authors: Cedric Lothritz, Bertrand Lebichot, Kevin Allix, Lisa Veiber, Tegawende Bissyande, Jacques Klein, Andrey Boytsov, Clément Lefebvre, Anne Goujon
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Data Augmentation, Language Modeling, Language Modelling

Pre-trained Language Models such as BERT have become ubiquitous in NLP where they have achieved state-of-the-art performance in most NLP tasks. While these models are readily available for English and other widely spoken languages, they remain scarce for low-resource languages such as Luxembourgish. In this paper, we present LuxemBERT, a BERT model for the Luxembourgish language that we create using the following approach: we augment the pre-training dataset by considering text data from a closely related language that we partially translate using a simple and straightforward method. We are then able to produce the LuxemBERT model, which we show to be effective for various NLP tasks: it outperforms a simple baseline built with the available Luxembourgish text data as well the multilingual mBERT model, which is currently the only option for transformer-based language models in Luxembourgish. Furthermore, we present datasets for various downstream NLP tasks that we created for this study and will make available to researchers on request.
A Systematic Approach to Derive a Refined Speech Corpus for Sinhala
Authors: Disura Warusawithana, Nilmani Kulaweera, Lakshan Weerasinghe, Buddhika Karunarathne
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: speech-recognition, Speech Recognition

Speech Recognition is an active research area where advances of technology have continuously driven the development of research work. However, due to the lack of adequate resources, certain languages such as Sinhala, are left to underutilize the technology. With techniques such as crowdsourcing and web scraping, several Sinhala corpora have been created and made publicly available. Despite them being large and generic, the correctness and consistency in their text data remain questionable, especially due to the lack of uniformity in the language used in the different sources of web scraped text. Addressing that requires a thorough understanding of technical and linguistic particulars pertaining to the language, which often leaves the issue unattended. We have followed a systematic approach to derive a refined corpus using a publicly available corpus for Sinhala speech recognition. In particular, we standardized the transcriptions of the corpus by removing noise in the text. Further, we applied corrections based on Sinhala linguistics. A comparative experiment shows a promising effect of the linguistic corrections by having a relative reduction of the Word-Error-Rate by 15.9%.
IgboBERT Models: Building and Training Transformer Models for the Igbo Language
Authors: Chiamaka Chukwuneke, Ignatius Ezeani, Paul Rayson, Mahmoud El-Haj
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This work presents a standard Igbo named entity recognition (IgboNER) dataset as well as the results from training and fine-tuning state-of-the-art transformer IgboNER models. We discuss the process of our dataset creation - data collection and annotation and quality checking. We also present experimental processes involved in building an IgboBERT language model from scratch as well as fine-tuning it along with other non-Igbo pre-trained models for the downstream IgboNER task. Our results show that, although the IgboNER task benefited hugely from fine-tuning large transformer model, fine-tuning a transformer model built from scratch with comparatively little Igbo text data seems to yield quite decent results for the IgboNER task. This work will contribute immensely to IgboNLP in particular as well as the wider African and low-resource NLP efforts Keywords: Igbo, named entity recognition, BERT models, under-resourced, dataset
Latvian National Corpora Collection – Korpuss.lv
Authors: Baiba Saulite, Roberts Darģis, Normunds Gruzitis, Ilze Auzina, Kristīne Levāne-Petrova, Lauma Pretkalniņa, Laura Rituma, Peteris Paikens, Arturs Znotins, Laine Strankale, Kristīne Pokratniece, Ilmārs Poikāns, Guntis Barzdins, Inguna Skadiņa, Anda Baklāne, Valdis Saulespurēns, Jānis Ziediņš
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Cultural Vocal Bursts Intensity Prediction, Language Modelling

LNCC is a diverse collection of Latvian language corpora representing both written and spoken language and is useful for both linguistic research and language modelling. The collection is intended to cover diverse Latvian language use cases and all the important text types and genres (e.g. news, social media, blogs, books, scientific texts, debates, essays, etc.), taking into account both quality and size aspects. To reach this objective, LNCC is a continuous multi-institutional and multi-project effort, supported by the Digital Humanities and Language Technology communities in Latvia. LNCC includes a broad range of Latvian texts from the Latvian National Library, Culture Information Systems Centre, Latvian National News Agency, Latvian Parliament, Latvian web crawl, various Latvian publishers, and from the Latvian language corpora created by Institute of Mathematics and Computer Science and its partners, including spoken language corpora. All corpora of LNCC are re-annotated with a uniform morpho-syntactic annotation scheme which enables federated search and consistent linguistics analysis in all the LNCC corpora, as well as facilitates to select and mix various corpora for pre-training large Latvian language models like BERT and GPT.
Investigating the Relationship Between Romanian Financial News and Closing Prices from the Bucharest Stock Exchange
Authors: Ioan-Bogdan Iordache, Ana Sabina Uban, Catalin Stoean, Liviu P. Dinu
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Translation

A new data set is gathered from a Romanian financial news website for the duration of four years. It is further refined to extract only information related to one company by selecting only paragraphs and even sentences that referred to it. The relation between the extracted sentiment scores of the texts and the stock prices from the corresponding dates is investigated using various approaches like the lexicon-based Vader tool, Financial BERT, as well as Transformer-based models. Automated translation is used, since some models could be only applied for texts in English. It is encouraging that all models, be that they are applied to Romanian or English texts, indicate a correlation between the sentiment scores and the increase or decrease of the stock closing prices.
A Free/Open-Source Morphological Analyser and Generator for Sakha
Authors: Sardana Ivanova, Jonathan Washington, Francis Tyers
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

We present, to our knowledge, the first ever published morphological analyser and generator for Sakha, a marginalised language of Siberia. The transducer, developed using HFST, has coverage of solidly above 90%, and high precision. In the development of the analyser, we have expanded linguistic knowledge about Sakha, and developed strategies for complex grammatical patterns. The transducer is already being used in downstream tasks, including computer assisted language learning applications for linguistic maintenance and computational linguistic shared tasks.
An Expanded Finite-State Transducer for Tsuut’ina Verbs
Authors: Joshua Holden, Christopher Cox, Antti Arppe
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This paper describes the expansion of a finite state transducer (FST) for the transitive verb system of Tsuut’ina (ISO 639-3: srs), a Dene (Athabaskan) language spoken in Alberta, Canada. Dene languages have unique templatic morphology, in which lexical, inflectional and derivational tiers are interlaced. Drawing on data from close to 9,000 verbal forms, the expanded model can handle a great range of common and rare argument structure types, including ditransitive and uniquely Dene object experiencer verbs. While challenges of speed remain, this expansion shows the ability of FST modelling to handle morphology of this type, and the expnded FST shows great promise for community language applications such as a morphologically informed online dictionary and word predictor, and for further FST development.This paper describes the expansion of a finite state transducer (FST) for the transitive verb system of Tsuut’ina (ISO 639-3: srs), a Dene (Athabaskan) language spoken in Alberta, Canada. Dene languages have unique templatic morphology, in which lexical, inflectional and derivational tiers are interlaced. Drawing on data from over 12,000 verbs forms, the expanded model can handle a great range of common and rare argument structure types, including ditransitive and uniquely Dene object experiencer verbs. While challenges of speed remain, this expansion shows the ability of FST modelling to handle morphology of this type, and the expnded FST shows great promise for community language applications such as a morphologically informed online dictionary and word predictor, and for further FST development.
Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction
Authors: Mehdi Mirzapour, Waleed Ragheb, Mohammad Javad Saeedizade, Kevin Cousot, Helene Jacquenet, Lawrence Carbon, Mathieu Lafourcade
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: 16k, Benchmarking, Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Machine Translation, Prediction, Question Answering, Recommendation Systems, Translation

Knowledge graphs applications, in industry and academia, motivate substantial research directions towards large-scale information extraction from various types of resources. Nowadays, most of the available knowledge graphs are either in English or multilingual. In this paper, we introduce RezoJDM16k, a French knowledge graph dataset based on RezoJDM. With 16k nodes, 832k triplets, and 53 relation types, RezoJDM16k can be employed in many NLP downstream tasks for the French language such as machine translation, question-answering, and recommendation systems. Moreover, we provide strong knowledge graph embedding baselines that are used in link prediction tasks for future benchmarking. Compared to the state-of-the-art English knowledge graph datasets used in link prediction, RezoJDM16k shows a similar promising predictive behavior.
The Badalona Corpus - An Audio, Video and Neuro-Physiological Conversational Dataset
Authors: Philippe Blache, Salomé Antoine, Dorina De Jong, Lena-Marie Huttner, Emilia Kerr, Thierry Legou, Eliot Maës, Clément François
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: EEG, Electroencephalogram (EEG)

We present in this paper the first natural conversation corpus recorded with all modalities and neuro-physiological signals. 5 dyads (10 participants) have been recorded three times, during three sessions (30mns each) with 4 days interval. During each session, audio and video are captured as well as the neural signal (EEG with Emotiv-EPOC) and the electro-physiological one (with Empatica-E4). This resource original in several respects. Technically, it is the first one gathering all these types of data in a natural conversation situation. Moreover, the recording of the same dyads at different periods opens the door to new longitudinal investigations such as the evolution of interlocutors’ alignment during the time. The paper situates this new type of resources with in the literature, presents the experimental setup and describes different annotations enriching the corpus.
Reading Time and Vocabulary Rating in the Japanese Language: Large-Scale Japanese Reading Time Data Collection Using Crowdsourcing
Authors: Masayuki Asahara
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This study examines how differences in human vocabulary affect reading time. Specifically, we assumed vocabulary to be the random effect of research participants when applying a generalized linear mixed model to the ratings of participants in the word familiarity survey. Thereafter, we asked the participants to take part in a self-paced reading task to collect their reading times. Through fixed effect of vocabulary when applying a generalized linear mixed model to reading time, we clarified the tendency that vocabulary differences give to reading time.
ChiSense-12: An English Sense-Annotated Child-Directed Speech Corpus
Authors: Francesco Cabiddu, Lewis Bott, Gary Jones, Chiara Gambi
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Language Acquisition, Word Sense Disambiguation

Language acquisition research has benefitted from the use of annotated corpora of child-directed speech to examine key questions about how children learn and process language in real-world contexts. However, a lack of sense-annotated corpora has limited investigations of child word sense disambiguation in naturalistic contexts. In this work, we sense-tagged 53 corpora of American and English speech directed to 958 target children up to 59 months of age, comprising a large-scale sample of 15,581 utterances for 12 ambiguous words. Importantly, we carefully selected target senses that we know - from previous investigations - young children understand. As such work was part of a project focused on investigating the role of verbs in child word sense disambiguation, we additionally coded for verb instances which took a target ambiguous word as verb object. We present experimental work where we leveraged our sense-tagged corpus ChiSense-12 to examine the role of verb-event structure in child word sense disambiguation, and we outline our plan to use Transformer-based computational architectures to test hypotheses on the role of different learning mechanisms underlying children word sense disambiguation performance.
Making People Laugh like a Pro: Analysing Humor Through Stand-Up Comedy
Authors: Beatrice Turano, Carlo Strapparava
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

The analysis of humor using computational tools has gained popularity in the past few years, and a lot of resources have been built for this purpose. However, most of these resources focus on standalone jokes or on occasional humorous sentences during presentations. In this paper I present a new dataset, SCRIPTS, built using stand-up comedy shows transcripts: the humor that this dataset collects is inserted in a larger narrative, composed of daily events made humorous by the ability of the comedian. This different perspective on the humor problem can allow us to think and study humor in a different way and possibly to open the path to new lines of research.
Testing Focus and Non-at-issue Frameworks with a Question-under-Discussion-Annotated Corpus
Authors: Christoph Hesse, Maurice Langner, Ralf Klabunde, Anton Benz
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

We present an annotated corpus of German driving reports for the analysis of Question-under-Discussion (QUD) based information structural distinctions. Since QUDs can hardly be defined in advance for providing a corresponding tagset, several theoretical issues arise concerning the scope and quality of the corpus and the development of an appropriate annotation tool for creating the corpus. We developed the corpus for testing the adequacy of QUD-based pragmatic frameworks of information structure. First analyses of the annotated information structures show that focus-related meaning aspects are essentially confirmed, indicating a sufficent accuracy of the annotations. Assumptions on non-at-issueness expressed by non-restrictive relative clauses made in the literature seem to be too strong, given the corpus data.
Development of a Multilingual CCG Treebank via Universal Dependencies Conversion
Authors: Tu-Anh Tran, Yusuke Miyao
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This paper introduces an algorithm to convert Universal Dependencies (UD) treebanks to Combinatory Categorial Grammar (CCG) treebanks. As CCG encodes almost all grammatical information into the lexicon, obtaining a high-quality CCG derivation from a dependency tree is a challenging task. Our algorithm relies on hand-crafted rules to assign categories to constituents, and a non-statistical parser to derive full CCG parses given the assigned categories. To evaluate our converted treebanks, we perform lexical, sentential, and syntactic rule coverage analysis, as well as CCG parsing experiments. Finally, we discuss how our method handles complex constructions, and propose possible future extensions.
The Automatic Extraction of Linguistic Biomarkers as a Viable Solution for the Early Diagnosis of Mental Disorders
Authors: Gloria Gagliardi, Fabio Tamburini
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

Digital Linguistic Biomarkers extracted from spontaneous language productions proved to be very useful for the early detection of various mental disorders. This paper presents a computational pipeline for the automatic processing of oral and written texts: the tool enables the computation of a rich set of linguistic features at the acoustic, rhythmic, lexical, and morphosyntactic levels. Several applications of the instrument - for the detection of Mild Cognitive Impairments, Anorexia Nervosa, and Developmental Language Disorders - are also briefly discussed.
Singlish Where Got Rules One? Constructing a Computational Grammar for Singlish
Authors: Siew Yeng Chow, Francis Bond
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

Singlish is a variety of English spoken in Singapore. In this paper, we share some of its grammar features and how they are implemented in the construction of a computational grammar of Singlish as a branch of English grammar. New rules were created and existing ones from standard English grammar of the English Resource Grammar (ERG) were changed in this branch to cater to how Singlish works. In addition, Singlish lexicon was added into the grammar together with some new lexical types. We used Head-driven Phrase Structure Grammar (HPSG) as the framework for this project of a creating a working computational grammar. As part of building the language resource, we also collected and formatted some data from the internet as part of a test suite for Singlish. Finally, the computational grammar was tested against a set of gold standard trees and compared with the standard English grammar to find out how well the grammar fares in analysing Singlish.