ymoslem/acl-abstracts huggingface dataset:
Investigating Political Ideologies through the Greek ParlaMint corpus (2024)

This paper has two objectives: to present (a) the creation of ParlaMint-GR, the Greek part of the ParlaMint corpora of debates in the parliaments of Europe, and (b) preliminary results on its comparison with a corpus of Greek party manifestos, aiming at the investigation of the ideologies of the Greek political parties and members of the Parliament. Additionally, a gender related comparison is explored. The creation of the ParlaMint-GR corpus is discussed, together with the solutions adopted for various challenges faced. The corpus of party manifestos, available through CLARIN:EL, serves for a comparative study with the corpus of speeches delivered by the members of the Greek Parliament, with the aim to identify the ideological positions of parties and politicians.
ParlaMint in TEITOK (2024)

This paper describes the ParlaMint 4.0 parliamentary corpora as made available in TEITOK at LINDAT. The TEITOK interface makes it possible to search through the corpus, to view each session in a readable manner, and to explore the names in the corpus. The interface does not present any new data, but provides an access point to the ParlaMint corpus that is less oriented to linguistic use only, and more accessible for the general public or researchers from other fields.
Historical Parliamentary Corpora Viewer (2024)

Historical parliamentary debates offer a window into the past and provide valuable insights for academic research and historical analysis. This paper presents a novel web application tailored to the exploration of historical parliamentary corpora in the context of Slovenian national identity. The developed web viewer enables advanced search functions within collections of historical parliamentary records and has an intuitive and user-friendly interface. Users can enter search terms and apply filters to refine their search results. The search function allows keyword and phrase searching, including the ability to search by delegate and place names. It is also possible to search for translations of the text by selecting the desired languages. The search results are displayed with a preview of the proceedings and highlighted phrases that match the search query. To review a specific record, the full PDF document can be displayed in a separate view, allowing the user to scroll through the PDF document and search the content. In addition, the two corpora of Slovenian historical records integrated into the viewer{---}the Carniolan Provincial Assembly Corpus and the Parliamentary Corpus of the First Yugoslavia{---}are described and an insight into the corresponding preparation processes is provided.
The dbpedia R Package: An Integrated Workflow for Entity Linking (for ParlaMint Corpora) (2024)

Entity Linking is a powerful approach for linking textual data to established structured data such as survey data or adminstrative data. However, in the realm of social science, the approach is not widely adopted. We argue that this is, at least in part, due to specific setup requirements which constitute high barriers for usage and workflows which are not well integrated into analyitical scenarios commonly deployed in social science research. We introduce the dbpedia R package to make the approach more accessible. It has a focus on functionality that is easily adoptable to the needs of social scientists working with textual data, including the support of different input formats, limited setup costs and various output formats. Using a ParlaMint corpus, we show the applicability and flexibility of the approach for parliamentary debates.
Video Retrieval System Using Automatic Speech Recognition for the Japanese Diet (2024)

The Japanese House of Representatives, one of the two houses of the Diet, has adopted an Automatic Speech Recognition (ASR) system, which directly transcribes parliamentary speech with an accuracy of 95 percent. The ASR system also provides a timestamp for every word, which enables retrieval of the video segments of the Parliamentary meetings. The video retrieval system we have developed allows one to pinpoint and play the parliamentary video clips corresponding to the meeting minutes by keyword search. In this paper, we provide its overview and suggest various ways we can utilize the system. The system is currently extended to cover meetings of local governments, which will allow us to investigate dialectal linguistic variations.
One Year of Continuous and Automatic Data Gathering from Parliaments of European Union Member States (2024)

This paper provides insight into automatic parliamentary corpora development. One year ago, I created a simple set of tools designed to continuously and automatically download, process, and create corpora from speeches in the parliaments of European Union member states. Despite the existence of numerous corpora providing speeches from European Union parliaments, the tools are more focused on collecting and building such corpora with minimal human interaction. These tools have been operating continuously for over a year, gathering parliamentary data and extending corpora, which together have more than one billion words. However, the process of maintaining these tools has brought unforeseen challenges, including issues such as being blocked by some parliaments due to overloading the parliament with requests, the inability to access the most recent data of a parliament, and effectively managing interrupted connections. Additionally, potential problems that may arise in the future are provided, along with possible solutions. These include problems with data loss prevention and adaptation to changes in the sources from which speeches are downloaded.
Government and Opposition in Danish Parliamentary Debates (2024)

In this paper, we address government and opposition speeches made by the Danish Parliament`s members from 2014 to 2022. We use the linguistic annotations and metadata in ParlaMint-DK, one of the ParlaMint corpora, to investigate some characteristics of the transcribed speeches made by government and opposition and test how well classifiers can identify the speeches delivered by these groups. Our analyses confirm that there are differences in the speeches made by government and opposition e.g., in the frequency of some modality expressions. In our study, we also include parties, which do not directly support or are against the government, the {\textquotedblleft}other{\textquotedblright} group. The best performing classifier for identifying speeches made by parties in government, in opposition or in {\textquotedblleft}other{\textquotedblright} is a transformer with a pre-trained Danish BERT model which gave an F1-score of 0.64. The same classifier obtained an F1-score of 0.77 on the binary identification of speeches made by government or opposition parties.
A new Resource and Baselines for Opinion Role Labelling in German Parliamentary Debates (2024)

Detecting opinions, their holders and targets in parliamentary debates provides an interesting layer of analysis, for example, to identify frequent targets of opinions for specific topics, actors or parties. In the paper, we present GePaDe-ORL, a new dataset for German parliamentary debates where subjective expressions, their opinion holders and targets have been annotated. We describe the annotation process and report baselines for predicting those annotations in our new dataset.
ParlaMint Widened: a European Dataset of Freedom of Information Act Documents (Position Paper) (2024)

This position paper makes an argument for creating a corpus similar to that of ParlaMint, not consisting of parliamentary proceedings, but of documents released under Freedom of Information Acts. Over 100 countries have such an act, and almost all European countries. Bringing these now dispersed document collections together in a uniform format into one portal will result in a valuable language resource. Besides that, our Dutch experience shows that such new larger exposure of these documents leads to efforts to improve their quality at the sources. Keywords: Freedom of Information Act, ParlaMint, Government Data
AraTar: A Corpus to Support the Fine-grained Detection of Hate Speech Targets in the Arabic Language (2024)

We are currently witnessing a concerning surge in the spread of hate speech across various social media platforms, targeting individuals or groups based on their protected characteristics such as race, religion, nationality and gender. This paper focuses on the detection of hate type (Task 1) and hate target (Task 2) in the Arabic language. To comprehensively address this problem, we have combined and re-annotated hate speech tweets from existing publicly available corpora, resulting in the creation of AraTar, the first and largest Arabic corpus annotated with support for multi-label classification for both hate speech types and target detection with a high inter-annotator agreement. Additionally, we sought to determine the most effective machine learning-based approach for addressing this issue. To achieve this, we compare and evaluate different approaches, including: (1) traditional machine learning-based models, (2) deep learning-based models fed with contextual embeddings, and (3) fine-tuning language models (LMs). Our results demonstrate that fine-tuning LMs, specifically using AraBERTv0.2-twitter (base), achieved the highest performance, with a micro-averaged F1-score of 84.5{\%} and 85.03{\%}, and a macro-averaged F1-score of 77.46{\%} and 73.15{\%}, for Tasks 1 and 2, respectively.
CLEANANERCorp: Identifying and Correcting Incorrect Labels in the ANERcorp Dataset (2024)

Label errors are a common issue in machine learning datasets, particularly for tasks such as Named Entity Recognition. Such label erros might hurt model training, affect evaluation results, and lead to an inaccurate assessment of model performance. In this study, we dived deep into one of the widely adopted Arabic NER benchmark datasets (ANERcorp) and found a significant number of annotation errors, missing labels, and inconsistencies. Therefore, in this study, we conducted empirical research to understand these erros, correct them and propose a cleaner version of the dataset named CLEANANERCorp. CLEANANERCorp will serve the research community as a more accurate and consistent benchmark.
Munazarat 1.0: A Corpus of Arabic Competitive Debates (2024)

This paper introduces the Corpus of Arabic Competitive Debates (Munazarat). Despite the significance of competitive debating as an activity of fostering critical thinking and promoting dialogue, researchers within the fields of Arabic Natural Language Processing (NLP), linguistics, argumentation studies, and education have access to very limited datasets about competitive debating. At this study stage, we introduce Munazarat 1.0, which combines recordings of approximately 50 hours collected from 73 debates at QatarDebate-recognized tournaments, where all of those debates were available on YouTube. Munazarat is a novel specialized speech Arabic corpus, mostly in Modern Standard Arabic (MSA), consisting of diverse debating topics and showing rich metadata for each debate. The transcription of debates was done using Fenek, a speech-to-text Kanari AI tool, and three native Arabic speakers reviewed each transcription file to enhance the quality provided by the machine. The Munazarat 1.0 dataset can be used to train Arabic NLP tools, develop an argumentation mining machine, and analyze Arabic argumentation and rhetoric styles. Keywords: Arabic Speech Corpus, Modern Standard Arabic, Debates
Leveraging Corpus Metadata to Detect Template-based Translation: An Exploratory Case Study of the Egyptian Arabic Wikipedia Edition (2024)

Wikipedia articles (content pages) are commonly used corpora in Natural Language Processing (NLP) research, especially in low-resource languages other than English. Yet, a few research studies have studied the three Arabic Wikipedia editions, Arabic Wikipedia (AR), Egyptian Arabic Wikipedia (ARZ), and Moroccan Arabic Wikipedia (ARY), and documented issues in the Egyptian Arabic Wikipedia edition regarding the massive automatic creation of its articles using template-based translation from English to Arabic without human involvement, overwhelming the Egyptian Arabic Wikipedia with articles that do not only have low-quality content but also with articles that do not represent the Egyptian people, their culture, and their dialect. In this paper, we aim to mitigate the problem of template translation that occurred in the Egyptian Arabic Wikipedia by identifying these template-translated articles and their characteristics through exploratory analysis and building automatic detection systems. We first explore the content of the three Arabic Wikipedia editions in terms of density, quality, and human contributions and utilize the resulting insights to build multivariate machine learning classifiers leveraging articles' metadata to detect the template-translated articles automatically. We then publicly deploy and host the best-performing classifier as an online application called {\textquoteleft}Egyptian Wikipedia Scanner' and release the extracted, filtered, labeled, and preprocessed datasets to the research community to benefit from our datasets and the online, web-based detection system.
A Novel Approach for Root Selection in the Dependency Parsing (2024)

Although syntactic analysis using the sequence labeling method is promising, it can be problematic when the labels sequence does not contain a root label. This can result in errors in the final parse tree when the postprocessing method assumes the first word as the root. In this paper, we present a novel postprocessing method for BERT-based dependency parsing as sequence labeling. Our method leverages the root`s part of speech tag to select a more suitable root for the dependency tree, instead of using the default first token. We conducted experiments on nine dependency treebanks from different languages and domains, and demonstrated that our technique consistently improves the labeled attachment score (LAS) on most of them.
AraMed: Arabic Medical Question Answering using Pretrained Transformer Language Models (2024)

Medical Question Answering systems have gained significant attention in recent years due to their potential to enhance medical decision-making and improve patient care. However, most of the research in this field has focused on English-language datasets, limiting the generalizability of MQA systems to non-English speaking regions. This study introduces AraMed, a large-scale Arabic Medical Question Answering dataset addressing the limited resources available for Arabic medical question answering. AraMed comprises of 270k question-answer pairs based on health consumer questions submitted to online medical forum. Experiments using various deep learning models showcase the dataset`s effectiveness, particularly with AraBERT models achieving highest results, specifically AraBERTv2 obtained an F1 score of 96.73{\%} in the answer selection task. The comparative analysis of different deep learning models provides insights into their strengths and limitations. These findings highlight the potential of AraMed for advancing Arabic medical question answering research and development.
The Multilingual Corpus of World`s Constitutions (MCWC) (2024)

The {\textquotedblleft}Multilingual Corpus of World`s Constitutions{\textquotedblright} (MCWC) serves as a valuable resource for the NLP community, offering a comprehensive collection of constitutions from around the world. Its focus on data quality and breadth of coverage enables advanced research in constitutional analysis, machine translation, and cross-lingual legal studies. The MCWC prepares its data to ensure high quality and minimal noise, while also providing valuable mappings of constitutions to their respective countries and continents, facilitating comparative analysis. Notably, the corpus offers pairwise sentence alignments across languages, supporting machine translation experiments. We utilise a leading Machine Translation model, fine-tuned on the MCWC to achieve accurate and context-aware translations. Additionally, we introduce an independent Machine Translation model as a comparative baseline. Fine-tuning the model on the MCWC improves accuracy, highlighting the significance of such a legal corpus for NLP and Machine Translation. The MCWC`s rich multilingual content and rigorous data quality standards raise the bar for legal text analysis and inspire innovation in the NLP community, opening new avenues for studying constitutional texts and multilingual data analysis.
TafsirExtractor: Text Preprocessing Pipeline preparing Classical Arabic Literature for Machine Learning Applications (2024)

In this paper, we present a comprehensive tool of preprocessing Classical Arabic (CA) literature in the field of historical exegetical studies for machine learning (ML) evaluations. Most recent ML models require the training data to be in a specific format (e.g. XML, TEI, CoNLL) to use it afterwards for ML applications such as Named Entity Recognition (NER) or Topic Modeling (TM). We report on how our method works and can be applied by other researchers with similar endeavors. Thereby, the importance of this comprehensive tool of preprocessing is demonstrated, as this novel approach has no predecessors for CA yet. We achieve results that enable the training of current ML models leading to state-of-the art performance for NER and TM on CA literature. We make our tool along its source code and data freely available for the Natural Language Processing (NLP) research community.
Advancing the Arabic WordNet: Elevating Content Quality (2024)

High-quality WordNets are crucial for achieving high-quality results in NLP applications that rely on such resources. However, the wordnets of most languages suffer from serious issues of correctness and completeness with respect to the words and word meanings they define, such as incorrect lemmas, missing glosses and example sentences, or an inadequate, Western-centric representation of the morphology and the semantics of the language. Previous efforts have largely focused on increasing lexical coverage while ignoring other qualitative aspects. In this paper, we focus on the Arabic language and introduce a major revision of the Arabic WordNet that addresses multiple dimensions of lexico-semantic resource quality. As a result, we updated more than 58{\%} of the synsets of the existing Arabic WordNet by adding missing information and correcting errors. In order to address issues of language diversity and untranslatability, we also extended the wordnet structure by new elements: phrasets and lexical gaps.
Arabic Speech Recognition of zero-resourced Languages: A case of Shehri (Jibbali) Language (2024)

Many under-resourced languages lack computational resources for automatic speech recognition (ASR) due to data scarcity issues. This makes developing accurate ASR models challenging. Shehri or Jibbali, spoken in Oman, lacks extensive annotated speech data. This paper aims to improve an ASR model for this under-resourced language. We collected a Shehri (Jibbali) speech corpus and utilized transfer learning by fine-tuning pre-trained ASR models on this dataset. Specifically, models like Wav2Vec2.0, HuBERT and Whisper were fine-tuned using techniques like parameter-efficient fine-tuning. Evaluation using word error rate (WER) and character error rate (CER) showed that the Whisper model, fine-tuned on the Shehri (Jibbali) dataset, significantly outperformed other models, with the best results from Whisper-medium achieving 3.5{\%} WER. This demonstrates the effectiveness of transfer learning for resource-constrained tasks, showing high zero-shot performance of pre-trained models.
OSACT6 Dialect to MSA Translation Shared Task Overview (2024)

This paper presents the Dialectal Arabic (DA) to Modern Standard Arabic (MSA) Machine Translation (MT) shared task in the sixth Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT6). The paper describes the creation of the validation and test data and the metrics used; and provides a brief overview of the submissions to the shared task. In all, 29 teams signed up and 6 teams made actual submissions. The teams used a variety of datasets and approaches to build their MT systems. The most successful submission involved using zero-shot and n-shot prompting of chatGPT.
OSACT 2024 Task 2: Arabic Dialect to MSA Translation (2024)

We present the results of Shared Task {\textquotedblleft}Dialect to MSA Translation{\textquotedblright}, which tackles challenges posed by the diverse Arabic dialects in machine translation. Covering Gulf, Egyptian, Levantine, Iraqi and Maghrebi dialects, the task offers 1001 sentences in both MSA and dialects for fine-tuning, alongside 1888 blind test sentences. Leveraging GPT-3.5, a state-of-the-art language model, our method achieved the a BLEU score of 29.61. This endeavor holds significant implications for Neural Machine Translation (NMT) systems targeting low-resource langu ages with linguistic variation. Additionally, negative experiments involving fine-tuning AraT5 and No Language Left Behind (NLLB) using the MADAR Dataset resulted in BLEU scores of 10.41 and 11.96, respectively. Future directions include expanding the dataset to incorporate more Arabic dialects and exploring alternative NMT architectures to further enhance translation capabilities.
ASOS at OSACT6 Shared Task: Investigation of Data Augmentation in Arabic Dialect-MSA Translation (2024)

The translation between Modern Standard Arabic (MSA) and the various Arabic dialects presents unique challenges due to the significant linguistic, cultural, and contextual variations across the regions where Arabic is spoken. This paper presents a system description of our participation in the OSACT 2024 Dialect to MSA Translation Shared Task. We explain our comprehensive approach which combines data augmentation techniques using generative pre-trained transformer models (GPT-3.5 and GPT-4) with fine-tuning of AraT5 V2, a model specifically designed for Arabic translation tasks. Our methodology has significantly expanded the training dataset, thus improving the model`s performance across five major Arabic dialects, namely Gulf, Egyptian, Levantine, Iraqi, and Maghrebi. We have rigorously evaluated our approach, using BLEU score, to ensure translation accuracy, fluency, and the preservation of meaning. Our results showcase the effectiveness of our refined models in addressing the challenges posed by diverse Arabic dialects and Modern Standard Arabic (MSA), achieving a BLEU score of 80{\%} on the validation test set and 22.25{\%} on the blind test set. However, it`s important to note that while utilizing a larger dataset, such as Madar + Dev, resulted in significantly higher evaluation BLEU scores, the performance on the blind test set was relatively lower. This observation underscores the importance of dataset size in model training, revealing potential limitations in generalization to unseen data due to variations in data distribution and domain mismatches.