pwc-archive/papers-with-abstracts huggingface dataset:
Words.hk: A Comprehensive Cantonese Dictionary Dataset with Definitions, Translations and Transliterated Examples
Authors: Chaak-ming Lau, Grace Wing-yan Chan, Raymond Ka-wai Tse, Lilian Suet-ying Chan
Date: 2022-06-01 00:00:00
Proceeding: DCLRL (LREC) 2022 6
Tasks: Segmentation, Transliteration

This paper discusses the compilation of the words.hk Cantonese dictionary dataset, which was compiled through manual annotation over a period of 7 years. Cantonese is a low-resource language with limited tagged or manually checked resources, especially at the sentential level, and this dataset is an attempt to fill the gap. The dataset contains over 53,000 entries of Cantonese words, which comes with basic lexical information (Jyutping phonemic transcription, part-of-speech tags, usage tags), manually crafted definitions in Written Cantonese, English translations, and Cantonese examples with English translation and Jyutping transliterations. Special attention has been paid to handle character variants, so that unintended “character errors” (equivalent to typos in phonemic writing systems) are filtered out, and intra-speaker variants are handled. Fine details on word segmentation, character variant handling, definition crafting will be discussed. The dataset can be used in a wide range of natural language processing tasks, such as word segmentation, construction of semantic web and training of models for Cantonese transliteration.
LiSTra Automatic Speech Translation: English to Lingala Case Study
Authors: Salomon Kabongo Kabenamualu, Vukosi Marivate, Herman Kamper
Date: 2022-06-01 00:00:00
Proceeding: DCLRL (LREC) 2022 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), automatic-speech-translation, Decoder, Machine Translation, speech-recognition, Speech Recognition, Translation

In recent years there has been great interest in addressing the data scarcity of African languages and providing baseline models for different Natural Language Processing tasks (Orife et al., 2020). Several initiatives (Nekoto et al., 2020) on the continent uses the Bible as a data source to provide proof of concept for some NLP tasks. In this work, we present the Lingala Speech Translation (LiSTra) dataset, release a full pipeline for the construction of such dataset in other languages, and report baselines using both the traditional cascade approach (Automatic Speech Recognition - Machine Translation), and a revolutionary transformer based End-2-End architecture (Liu et al., 2020) with a custom interactive attention that allows information sharing between the recognition decoder and the translation decoder.
Ara-Women-Hate: An Annotated Corpus Dedicated to Hate Speech Detection against Women in the Arabic Community
Authors: Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi, Akram Abdelhaq Moumna
Date: 2022-06-01 00:00:00
Proceeding: DCLRL (LREC) 2022 6
Tasks: Hate Speech Detection

In this paper, an approach for hate speech detection against women in the Arabic community on social media (e.g. Youtube) is proposed. In the literature, similar works have been presented for other languages such as English. However, to the best of our knowledge, not much work has been conducted in the Arabic language. A new hate speech corpus (Arabic_fr_en) is developed using three different annotators. For corpus validation, three different machine learning algorithms are used, including deep Convolutional Neural Network (CNN), long short-term memory (LSTM) network and Bi-directional LSTM (Bi-LSTM) network. Simulation results demonstrate the best performa
Word-level Language Identification Using Subword Embeddings for Code-mixed Bangla-English Social Media Data
Authors: Aparna Dutta
Date: 2022-06-01 00:00:00
Proceeding: DCLRL (LREC) 2022 6
Tasks: Language Identification, POS

This paper reports work on building a word-level language identification (LID) model for code-mixed Bangla-English social media data using subword embeddings, with an ultimate goal of using this LID module as the first step in a modular part-of-speech (POS) tagger in future research. This work reports preliminary results of a word-level LID model that uses a single bidirectional LSTM with subword embeddings trained on very limited code-mixed resources. At the time of writing, there are no previous reported results available in which subword embeddings are used for language identification with the Bangla-English code-mixed language pair. As part of the current work, a labeled resource for word-level language identification is also presented, by correcting 85.7% of labels from the 2016 ICON Whatsapp Bangla-English dataset. The trained model was evaluated on a test set of 4,015 tokens compiled from the 2015 and 2016 ICON datasets, and achieved a test accuracy of 93.61%.
Introducing the Digital Language Equality Metric: Technological Factors
Authors: Federico Gaspari, Owen Gallagher, Georg Rehm, Maria Giagkou, Stelios Piperidis, Jane Dunne, Andy Way
Date: 2022-06-01 00:00:00
Proceeding: TDLE (LREC) 2022 6

This paper introduces the concept of Digital Language Equality (DLE) developed by the EU-funded European Language Equality (ELE) project, and describes the associated DLE Metric with a focus on its technological factors (TFs), which are complemented by situational contextual factors. This work aims at objectively describing the level of technological support of all European languages and lays the foundation to implement a large-scale EU-wide programme to ensure that these languages can continue to exist and prosper in the digital age, to serve the present and future needs of their speakers. The paper situates this ongoing work with a strong European focus in the broader context of related efforts, and explains how the DLE Metric can help track the progress towards DLE for all languages of Europe, focusing in particular on the role played by the TFs. These are derived from the European Language Grid (ELG) Catalogue, that provides the empirical basis to measure the level of digital readiness of all European languages. The DLE Metric scores can be consulted through an online interactive dashboard to show the level of technological support of each European language and track the overall progress toward DLE.
Introducing the Digital Language Equality Metric: Contextual Factors
Authors: Annika Grützner-Zahn, Georg Rehm
Date: 2022-06-01 00:00:00
Proceeding: TDLE (LREC) 2022 6

In our digital age, digital language equality is an important goal to enable participation in society for all citizens, independent of the language they speak. To assess the current state of play with regard to Europe’s languages, we developed, in the project European Language Equality, a metric for digital language equality that consists of two parts, technological and contextual (i.e., non-technological) factors. We present a metric for calculating the contextual factors for over 80 European languages. For each language, a score is calculated that reflects the broader context or socio-economic ecosystem of a language, which has, for a given language, a direct impact for technology and resource development; it is important to note, though, that Language Technologies and Resources related aspects are reflected by the technological factors. To reduce the vast number of potential contextual factors to an adequate number, five different configurations were calculated and evaluated with a panel of experts. The best results were achieved by a configuration in which 12 manually curated factors were included. In the factor selection process, attention was paid to data quality, automatic updatability, inclusion of data from different domains, and a balance between different data types. The evaluation shows that this specific configuration is stable for the official EU languages; while for regional and minority languages, as well as national non-official EU languages, there is room for improvement.
Collaborative Metadata Aggregation and Curation in Support of Digital Language Equality Monitoring
Authors: Maria Giagkou, Stelios Piperidis, Penny Labropoulou, Miltos Deligiannis, Athanasia Kolovou, Leon Voukoutis
Date: 2022-06-01 00:00:00
Proceeding: TDLE (LREC) 2022 6

The European Language Equality (ELE) project develops a strategic research, innovation and implementation agenda (SRIA) and a roadmap for achieving full digital language equality in Europe by 2030. Key component of the SRIA development is an accurate estimation of the current standing of languages with respect to their technological readiness. In this paper we present the empirical basis on which such estimation is grounded, its starting point and in particular the automatic and collaborative methods used for extending it. We focus on the collaborative expert activities, the challenges posed, and the solutions adopted. We also briefly present the dashboard application developed for querying and visualising the empirical data as well as monitoring and comparing the evolution of technological support within and across languages.
Measuring HLT Research Equality of European Languages
Authors: Gorka Artola, German Rigau
Date: 2022-06-01 00:00:00
Proceeding: TDLE (LREC) 2022 6

This work explores quantitative indicators that could potentially measure the equality and inequality research levels among the languages of the European Union in the field of human language technologies (HLT research equality). Our ultimate goal is to investigate European language equality in HLT research considering the number of papers published on several HLT research venues that mention each language with respect to their estimated number of speakers. This way, inequalities affecting HLT research in Europe will depend on other factors such as history, political status, GDP, level of social or technological development, etc. We have identified several groups of EU languages in the proposed measurement of HLT research equality, each group comprising languages with large differences in the number of speakers. We have discovered a relative equality among surprisingly different languages in terms of number of speakers and also relevant inequalities within the most spoken languages. All data and code will be released upon acceptance.
National Language Technology Platform for Public Administration
Authors: Marko Tadić, Daša Farkaš, Matea Filko, Artūrs Vasiļevskis, Andrejs Vasiļjevs, Jānis Ziediņš, Željka Motika, Mark Fishel, Hrafn Loftsson, Jón Guðnason, Claudia Borg, Keith Cortis, Judie Attard, Donatienne Spiteri
Date: 2022-06-01 00:00:00
Proceeding: TDLE (LREC) 2022 6

This article presents the work in progress on the collaborative project of several European countries to develop National Language Technology Platform (NLTP). The project aims at combining the most advanced Language Technology tools and solutions in a new, state-of-the-art, Artificial Intelligence driven, National Language Technology Platform for five EU/EEA official and lower-resourced languages.
The Nós Project: Opening routes for the Galician language in the field of language technologies
Authors: Iria de-Dios-Flores, Carmen Magariños, Adina Ioana Vladu, John E. Ortega, José Ramom Pichel, Marcos García, Pablo Gamallo, Elisa Fernández Rei, Alberto Bugarín-Diz, Manuel González González, Senén Barro, Xosé Luis Regueira
Date: 2022-06-01 00:00:00
Proceeding: TDLE (LREC) 2022 6
Tasks: Cultural Vocal Bursts Intensity Prediction, Machine Translation, Speech-to-Text, text-to-speech, Text to Speech, Translation

The development of language technologies (LTs) such as machine translation, text analytics, and dialogue systems is essential in the current digital society, culture and economy. These LTs, widely supported in languages in high demand worldwide, such as English, are also necessary for smaller and less economically powerful languages, as they are a driving force in the democratization of the communities that use them due to their great social and cultural impact. As an example, dialogue systems allow us to communicate with machines in our own language; machine translation increases access to contents in different languages, thus facilitating intercultural relations; and text-to-speech and speech-to-text systems broaden different categories of users’ access to technology. In the case of Galician (co-official language, together with Spanish, in the autonomous region of Galicia, located in northwestern Spain), incorporating the language into state-of-the-art AI applications can not only significantly favor its prestige (a decisive factor in language normalization), but also guarantee citizens’ language rights, reduce social inequality, and narrow the digital divide. This is the main motivation behind the Nós Project (Proxecto Nós), which aims to have a significant contribution to the development of LTs in Galician (currently considered a low-resource language) by providing openly licensed resources, tools, and demonstrators in the area of intelligent technologies.
An Analysis of Abusive Language Data Collected through a Game with a Purpose
Authors: Federico Bonetti, Sara Tonelli
Date: 2022-06-01 00:00:00
Proceeding: games (LREC) 2022 6
Tasks: Abusive Language

In this work we present an analysis of abusive language annotations collected through a 3D video game. With this approach, we are able to involve in the annotation teenagers, i.e. typical targets of cyberbullying, whose data are usually not available for research purposes. Using the game in the framework of educational activities to empower teenagers against online abuse we are able to obtain insights into how teenagers communicate, and what kind of messages they consider more offensive. While players produced interesting annotations and the distributions of classes between players and experts are similar, we obtained a significant number of mismatching judgements between experts and players.
Applying Gamification Incentives in the Revita Language-learning System
Authors: Jue Hou, Ilmari Kylliäinen, Anisia Katinskaia, Giacomo Furlan, Roman Yangarber
Date: 2022-06-01 00:00:00
Proceeding: games (LREC) 2022 6

We explore the importance of gamification features in a language-learning platform designed for intermediate-to-advanced learners. Our main thesis is: learning toward advanced levels requires a massive investment of time. If the learner engages in more practice sessions, and if the practice sessions are longer, we can expect the results to be better. This principle appears to be tautologically self-evident. Yet, keeping the learner engaged in general—and building gamification features in particular—requires substantial efforts on the part of developers. Our goal is to keep the learner engaged in long practice sessions over many months—rather than for the short-term. This creates a conflict: In academic research on language learning, resources are typically scarce, and gamification usually is not considered an essential priority for allocating resources. We argue in favor of giving serious consideration to gamification in the language-learning setting—as a means of enabling in-depth research. In this paper, we introduce several gamification incentives in the Revita language-learning platform. We discuss the problems in obtaining quantitative measures of the effectiveness of gamification features.
Less Text, More Visuals: Evaluating the Onboarding Phase in a GWAP for NLP
Authors: Fatima Althani, Chris Madge, Massimo Poesio
Date: 2022-06-01 00:00:00
Proceeding: games (LREC) 2022 6
Tasks: Game Design

Games-with-a-purpose find attracting players a challenge. To improve player recruitment, we explored two game design elements that can increase player engagement during the onboarding phase; a narrative and a tutorial. In a qualitative study with 12 players of linguistic and language learning games, we examined the effect of presentation format on players’ engagement. Our reflexive thematic analysis found that in the onboarding phase of a GWAP for NLP, presenting players with visuals is expected and pre- senting too much text overwhelms them. Furthermore, players found that the instructions they were presented with lacked linguistic context. Additionally, the tutorial and game interface required refinement as the feedback is unsupportive and the graphics were not clear.
How NLP Can Strengthen Digital Game Based Language Learning Resources for Less Resourced Languages
Authors: Monica Ward, Liang Xu, Elaine Uí Dhonnchadha
Date: 2022-06-01 00:00:00
Proceeding: games (LREC) 2022 6

This paper provides an overview of the Cipher engine which enables the development of a Digital Educational Game (DEG) based on noticing ciphers or patterns in texts. The Cipher engine was used to develop the Cipher: Faoi Gheasa, a digital educational game for Irish, which incorporates NLP resources and is informed by Digital Game-Based Language Learning (DGBLL) and Computer-Assisted Language Learning (CALL) research. The paper outlines six phases where NLP has strengthened the Cipher: Faoi Gheasa game. It shows how the Cipher engine can be used to build a Cipher game for other languages, particularly low-resourced and endangered languages in which NLP resources are under-developed or few in number.
The “Actors Challenge” Project: Collecting Data on Intonation Profiles via a Web Game
Authors: Natallia Chaiko, Sia Sepanta, Roberto Zamparelli
Date: 2022-06-01 00:00:00
Proceeding: games (LREC) 2022 6

This paper describes ”Actors Challenge”, a soon-to-go-public web game where the players alternate in the double role of actors and judges of other players’ acted-out utterances, and in the process create an oral dataset of prosodic contours that can disambiguate textually identical utterances in different contexts. The game is undergoing alpha testing and should be deployed within a few months. We discuss the need, the core mechanism and the challenges ahead.
Generating Descriptive and Rules-Adhering Spells for Dungeons & Dragons Fifth Edition
Authors: Pax Newman, Yudong Liu
Date: 2022-06-01 00:00:00
Proceeding: games (LREC) 2022 6
Tasks: Descriptive

We examine the task of generating unique content for the spell system of the tabletop roleplaying game Dungeons and Dragons Fifth Edition using several generative language models. Due to the descriptive nature of the game Dungeons and Dragons Fifth Edition, it presents a number of interesting avenues for generation and analysis of text. In particular, the “spell” system of the game has interesting and unique characteristics as it is primarily made up of high level and descriptive text but has many of the game’s main rules embedded with that text. Thus, we examine the capabilities of several models on the task of generating new content for this game, evaluating the performance through the use of both score-based methods and a survey on the best performing model to determine how the generated content conforms to the rules of the game and how well they might be used in the game.
An NLP Approach for the Analysis of Global Reporting Initiative Indexes from Corporate Sustainability Reports
Authors: Marco Polignano, Nicola Bellantuono, Francesco Paolo Lagrasta, Sergio Caputo, Pierpaolo Pontrandolfo, Giovanni Semeraro
Date: 2022-06-01 00:00:00
Proceeding: CSRNLP (LREC) 2022 6

Sustainability reporting has become an annual requirement in many countries and for certain types of companies. Sustainability reports inform stakeholders about companies’ commitment to sustainable development and their economic, social, and environmental sustainability practices. However, the fact that norms and standards allow a certain discretion to be adopted by drafting organizations makes such reports hardly comparable in terms of layout, disclosures, key performance indicators (KPIs), and so on. In this work, we present a system based on natural language processing and information extraction techniques to retrieve relevant information from sustainability reports, compliant with the Global Reporting Initiative Standards, written in Italian and English language. Specifically, the system is able to identify references to the various sustainability topics discussed by the reports: on which page of the document those references have been found, the context of each reference, and if it is mentioned positively or negatively. The output of the system has been then evaluated against a ground truth obtained through a manual annotation process on 134 reports. Experimental outcomes highlight the affordability of the approach for improving sustainability disclosures, accessibility, and transparency, thus empowering stakeholders to conduct further analysis and considerations.
Tracking Changes in ESG Representation: Initial Investigations in UK Annual Reports
Authors: Matthew Purver, Matej Martinc, Riste Ichev, Igor Lončarski, Katarina Sitar Šuštar, Aljoša Valentinčič, Senja Pollak
Date: 2022-06-01 00:00:00
Proceeding: CSRNLP (LREC) 2022 6
Tasks: Language Modelling, Word Embeddings

We describe initial work into analysing the language used around environmental, social and governance (ESG) issues in UK company annual reports. We collect a dataset of annual reports from UK FTSE350 companies over the years 2012-2019; separately, we define a categorized list of core ESG terms (single words and multi-word expressions) by combining existing lists with manual annotation. We then show that this list can be used to analyse the changes in ESG language in the dataset over time, via a combination of language modelling and distributional modelling via contextual word embeddings. Initial findings show that while ESG discussion in annual reports is becoming significantly more likely over time, the increase varies with category and with individual terms, and that some terms show noticeable changes in usage.
A Corpus-based Study of Corporate Image Represented in Corporate Social Responsibility Report: A Case Study of China Mobile and Vodafone
Authors: Xing Chen, Liang Xu
Date: 2022-06-01 00:00:00
Proceeding: CSRNLP (LREC) 2022 6
Tasks: Cultural Vocal Bursts Intensity Prediction

By examination of the high-frequency nouns, verbs, and keywords, the present study probes into the similarities and differences of corporate images represented in Corporate Social Responsibility (CSR) reports of China Mobile and Vodafone. The results suggest that: 1) both China Mobile and Vodafone prefer using some positive words, like improve, support and service to shape a positive, approachable and easy-going corporate image, and an image of prioritizing the environmental sustainability and the well-being of people; 2) CSR reports of China Mobile contain the keywords poverty and alleviation, which means China Mobile is pragmatic, collaborative and active to assume the responsibility for social events; 3) CSR reports of Vodafone contain keywords like privacy, women and global as well as some other countries, which shows Vodafone is enterprising, globalized and attentive to the development of women; 4) these differences might be related to the ideology and social culture of Chinese and British companies. This study may contribute to understanding the function of CSR report and offer helpful implications for broadening the research of corporate image.