3 citations · 17 across the 45 of their papers we have counts for
48 papers
ROMPAR: Morphological Completion and Demographic Unlearning for Romanian-Accented Speech Recognition
Andrei-Marius Avram, Aureliu-Valentin Antonie, Ştefan-Bogdan Badea +3
Automated transcription of parliamentary proceedings faces significant hurdles due to demographic bias, dialectal variation, and technical artifacts such as utterance truncation du…
RoIt-XMASA: Multi-Domain Multilingual Sentiment Analysis Dataset for Romanian and Italian
Andrei-Marius Avram, Aureliu Valentin Antonie, Cosmin-Mircea Croitoru +2
We present RoIt-XMASA, a multilingual dataset that extends the Cross-lingual Multi-domain Amazon Sentiment Analysis to Italian and Romanian, comprising 36,000 labeled reviews acros…
RoLegalGEC: Legal Domain Grammatical Error Detection and Correction Dataset for Romanian
Mircea Timpuriu, Mihaela-Claudia Cercel, Dumitru-Clementin Cercel
The importance of clear and correct text in legal documents cannot be understated, and, consequently, a grammatical error correction tool meant to assist a professional in the law…
Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models
George-Andrei Dima, Răzvan-Alexandru Smădu, Dumitru-Clementin Cercel
Focusing on low-resource languages is an essential step toward democratizing generative AI. In this work, we contribute to reducing the multimodal NLP resource gap for Romanian. We…
RoCoISLR: A Romanian Corpus for Isolated Sign Language Recognition
Cătălin-Alexandru Rîpanu, Andrei-Theodor Hotnog, Giulia-Stefania Imbrea +1
Automatic sign language recognition plays a crucial role in bridging the communication gap between deaf communities and hearing individuals; however, most available datasets focus…
Air Pollution Forecasting in Bucharest
Dragoş-Andrei Şerban, Răzvan-Alexandru Smădu, Dumitru-Clementin Cercel
Air pollution, especially the particulate matter 2.5 (PM2.5), has become a growing concern in recent years, primarily in urban areas. Being exposed to air pollution is linked to de…
SeLeRoSa: Sentence-Level Romanian Satire Detection Dataset
Răzvan-Alexandru Smădu, Andreea Iuga, Dumitru-Clementin Cercel +1
Satire, irony, and sarcasm are techniques typically used to express humor and critique, rather than deceive; however, they can occasionally be mistaken for factual reporting, akin…
Multimodal Learning with Augmentation Techniques for Natural Disaster Assessment
Adrian-Dinu Urse, Dumitru-Clementin Cercel, Florin Pop
Natural disaster assessment relies on accurate and rapid access to information, with social media emerging as a valuable real-time source. However, existing datasets suffer from cl…
MoRoVoc: A Large Dataset for Geographical Variation Identification of the Spoken Romanian Language
Andrei-Marius Avram, Ema-Ioana Bănescu, Anda-Teodora Robea +2
This paper introduces MoRoVoc, the largest dataset for analyzing the regional variation of spoken Romanian. It has more than 93 hours of audio and 88,192 audio samples, balanced be…
IGAff: Benchmarking Adversarial Iterative and Genetic Affine Algorithms on Deep Neural Networks
Sebastian-Vasile Echim, Andrei-Alexandru Preda, Dumitru-Clementin Cercel +1
Deep neural networks currently dominate many fields of the artificial intelligence landscape, achieving state-of-the-art results on numerous tasks while remaining hard to understan…
RoD-TAL: A Benchmark for Answering Questions in Romanian Driving License Exams
Andrei Vlad Man, Răzvan-Alexandru Smădu, Cristian-George Craciun +3
The intersection of AI and legal systems presents a growing need for tools that support legal education, particularly in under-resourced languages such as Romanian. In this work, w…
MuSaRoNews: A Multidomain, Multimodal Satire Dataset from Romanian News Articles
Răzvan-Alexandru Smădu, Andreea Iuga, Dumitru-Clementin Cercel
Satire and fake news can both contribute to the spread of false information, even though both have different purposes (one if for amusement, the other is to misinform). However, it…
Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology Images
Andrei-Alexandru Preda, Iulian-Marius Tăiatu, Dumitru-Clementin Cercel
In the field of deep learning, large architectures often obtain the best performance for many tasks, but also require massive datasets. In the histological domain, tissue images ar…
SaRoHead: Detecting Satire in a Multi-Domain Romanian News Headline Dataset
Mihnea-Alexandru Vîrlan, Răzvan-Alexandru Smădu, Dumitru-Clementin Cercel +2
The primary goal of a news headline is to summarize an event in as few words as possible. Depending on the media outlet, a headline can serve as a means to objectively deliver a su…
UniBERT: Adversarial Training for Language-Universal Representations
Andrei-Marius Avram, Marian Lupaşcu, Dumitru-Clementin Cercel +2
This paper presents UniBERT, a compact multilingual language model that uses an innovative training framework that integrates three components: masked language modeling, adversaria…
RoLargeSum: A Large Dialect-Aware Romanian News Dataset for Summary, Headline, and Keyword Generation
Andrei-Marius Avram, Mircea Timpuriu, Andreea Iuga +6
Using supervised automatic summarisation methods requires sufficient corpora that include pairs of documents and their summaries. Similarly to many tasks in natural language proces…
GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answering
Cristian-George Crăciun, Răzvan-Alexandru Smădu, Dumitru-Clementin Cercel +1
Pre-trained Language Models (PLMs) have shown remarkable performances in recent years, setting a new paradigm for NLP research and industry. The legal domain has received some atte…
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups
Răzvan-Alexandru Smădu, David-Gabriel Ion, Dumitru-Clementin Cercel +2
Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own. Some variations of this binary classification t…
A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition
David-Gabriel Ion, Răzvan-Alexandru Smădu, Dumitru-Clementin Cercel +2
Best-performing speech models are trained on large amounts of data in the language they are meant to work for. However, most languages have sparse data, making training models chal…
RoQLlama: A Lightweight Romanian Adapted Language Model
George-Andrei Dima, Andrei-Marius Avram, Cristian-George Crăciun +1
The remarkable achievements obtained by open-source large language models (LLMs) in recent years have predominantly been concentrated on tasks involving the English language. In th…
Enhancing Romanian Offensive Language Detection through Knowledge Distillation, Multi-Task Learning, and Data Augmentation
Vlad-Cristian Matei, Iulian-Marius Tăiatu, Răzvan-Alexandru Smădu +1
This paper highlights the significance of natural language processing (NLP) within artificial intelligence, underscoring its pivotal role in comprehending and modeling human langua…
Investigating the Impact of Semi-Supervised Methods with Data Augmentation on Offensive Language Detection in Romanian Language
Elena-Beatrice Nicola, Dumitru-Clementin Cercel, Florin Pop
Offensive language detection is a crucial task in today's digital landscape, where online platforms grapple with maintaining a respectful and inclusive environment. However, buildi…
HistNERo: Historical Named Entity Recognition for the Romanian Language
Andrei-Marius Avram, Andreea Iuga, George-Vlad Manolache +8
This work introduces HistNERo, the first Romanian corpus for Named Entity Recognition (NER) in historical newspapers. The dataset contains 323k tokens of text, covering more than h…
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation
Sebastian-Vasile Echim, Iulian-Marius Tăiatu, Dumitru-Clementin Cercel +1
This work focuses on plant leaf disease classification and explores three crucial aspects: adversarial training, model explainability, and model compression. The models' robustness…