Vasile Păiș

Also published as: Vasile Pais, Vasile Păis


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Distilling the Knowledge of Romanian BERTs Using Multiple Teachers
Andrei-Marius Avram | Darius Catrina | Dumitru-Clementin Cercel | Mihai Dascalu | Traian Rebedea | Vasile Pais | Dan Tufis
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Running large-scale pre-trained language models in computationally constrained environments remains a challenging problem yet to be addressed, while transfer learning from these models has become prevalent in Natural Language Processing tasks. Several solutions, including knowledge distillation, network quantization, or network pruning have been previously proposed; however, these approaches focus mostly on the English language, thus widening the gap when considering low-resource languages. In this work, we introduce three light and fast versions of distilled BERT models for the Romanian language: Distil-BERT-base-ro, Distil-RoBERT-base, and DistilMulti-BERT-base-ro. The first two models resulted from the individual distillation of knowledge from two base versions of Romanian BERTs available in literature, while the last one was obtained by distilling their ensemble. To our knowledge, this is the first attempt to create publicly available Romanian distilled BERT models, which were thoroughly evaluated on five tasks: part-of-speech tagging, named entity recognition, sentiment analysis, semantic textual similarity, and dialect identification. Our experimental results argue that the three distilled models offer performance comparable to their teachers, while being twice as fast on a GPU and ~35% smaller. In addition, we further test the similarity between the predictions of our students versus their teachers by measuring their label and probability loyalty, together with regression loyalty - a new metric introduced in this work.

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Improving Romanian BioNER Using a Biologically Inspired System
Maria Mitrofan | Vasile Pais
Proceedings of the 21st Workshop on Biomedical Language Processing

Recognition of named entities present in text is an important step towards information extraction and natural language understanding. This work presents a named entity recognition system for the Romanian biomedical domain. The system makes use of a new and extended version of SiMoNERo corpus, that is open sourced. Also, the best system is available for direct usage in the RELATE platform.

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An Open-Domain QA System for e-Governance
Radu Ion | Andrei-Marius Avram | Vasile Păis | Maria Mitrofan | Verginica Barbu Mititelu | Elena Irimia | Valentin Badea
Proceedings of the 5th International Conference on Computational Linguistics in Bulgaria (CLIB 2022)

The paper presents an open-domain Question Answering system for Romanian, answering COVID-19 related questions. The QA system pipeline involves automatic question processing, automatic query generation, web searching for the top 10 most relevant documents and answer extraction using a fine-tuned BERT model for Extractive QA, trained on a COVID-19 data set that we have manually created. The paper will present the QA system and its integration with the Romanian language technologies portal RELATE, the COVID-19 data set and different evaluations of the QA performance.

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RACAI@SMM4H’22: Tweets Disease Mention Detection Using a Neural Lateral Inhibitory Mechanism
Andrei-Marius Avram | Vasile Pais | Maria Mitrofan
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

This paper presents our system employed for the Social Media Mining for Health (SMM4H) 2022 competition Task 10 - SocialDisNER. The goal of the task was to improve the detection of diseases in tweets. Because the tweets were in Spanish, we approached this problem using a system that relies on a pre-trained multilingual model and is fine-tuned using the recently introduced lateral inhibition layer. We further experimented on this task by employing a conditional random field on top of the system and using a voting-based ensemble that contains various architectures. The evaluation results outlined that our best performing model obtained 83.7% F1-strict on the validation set and 82.1% F1-strict on the test set.

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Romanian micro-blogging named entity recognition including health-related entities
Vasile Pais | Verginica Barbu Mititelu | Elena Irimia | Maria Mitrofan | Carol Luca Gasan | Roxana Micu
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

This paper introduces a manually annotated dataset for named entity recognition (NER) in micro-blogging text for Romanian language. It contains gold annotations for 9 entity classes and expressions: persons, locations, organizations, time expressions, legal references, disorders, chemicals, medical devices and anatomical parts. Furthermore, word embeddings models computed on a larger micro-blogging corpus are made available. Finally, several NER models are trained and their performance is evaluated against the newly introduced corpus.

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Challenges in Creating a Representative Corpus of Romanian Micro-Blogging Text
Vasile Pais | Maria Mitrofan | Verginica Barbu Mititelu | Elena Irimia | Roxana Micu | Carol Luca Gasan
Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-10)

Following the successful creation of a national representative corpus of contemporary Romanian language, we turned our attention to the social media text, as present in micro-blogging platforms. In this paper, we present the current activities as well as the challenges faced when trying to apply existing tools (for both annotation and indexing) to a Romanian language micro-blogging corpus. These challenges are encountered at all annotation levels, including tokenization, and at the indexing stage. We consider that existing tools for Romanian language processing must be adapted to recognize features such as emoticons, emojis, hashtags, unusual abbreviations, elongated words (commonly used for emphasis in micro-blogging), multiple words joined together (within oroutside hashtags), and code-mixed text.

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RACAI at SemEval-2022 Task 11: Complex named entity recognition using a lateral inhibition mechanism
Vasile Pais
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper presents RACAI’s system used for the shared task of “Multilingual Complex Named Entity Recognition (MultiCoNER)”, organized as part of the “The 16th International Workshop on Semantic Evaluation (SemEval 2022)”. The system employs a novel layer inspired by the biological mechanism of lateral inhibition. This allowed the system to achieve good results without any additional resources apart from the provided training data. In addition to the system’s architecture, results are provided as well as observations regarding the provided dataset.

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Romanian Language Translation in the RELATE Platform
Vasile Pais | Maria Mitrofan | Andrei-Marius Avram
Proceedings of the Fifth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2022)

This paper presents the usage of the RELATE platform for translation tasks involving the Romanian language. Using this platform, it is possible to perform text and speech data translations, either for single documents or for entire corpora. Furthermore, the platform was successfully used in international projects to create new resources useful for Romanian language translation.

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Use Case: Romanian Language Resources in the LOD Paradigm
Verginica Barbu Mititelu | Elena Irimia | Vasile Pais | Andrei-Marius Avram | Maria Mitrofan
Proceedings of the 8th Workshop on Linked Data in Linguistics within the 13th Language Resources and Evaluation Conference

In this paper, we report on (i) the conversion of Romanian language resources to the Linked Open Data specifications and requirements, on (ii) their publication and (iii) interlinking with other language resources (for Romanian or for other languages). The pool of converted resources is made up of the Romanian Wordnet, the morphosyntactic and phonemic lexicon RoLEX, four treebanks, one for the general language (the Romanian Reference Treebank) and others for specialised domains (SiMoNERo for medicine, LegalNERo for the legal domain, PARSEME-Ro for verbal multiword expressions), frequency information on lemmas and tokens and word embeddings as extracted from the reference corpus for contemporary Romanian (CoRoLa) and a bi-modal (text and speech) corpus. We also present the limitations coming from the representation of the resources in Linked Data format. The metadata of LOD resources have been published in the LOD Cloud. The resources are available for download on our website and a SPARQL endpoint is also available for querying them.


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Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media
Vasile Pais | Maria Mitrofan
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

This paper presents our contribution to the ProfNER shared task. Our work focused on evaluating different pre-trained word embedding representations suitable for the task. We further explored combinations of embeddings in order to improve the overall results.

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Named Entity Recognition in the Romanian Legal Domain
Vasile Pais | Maria Mitrofan | Carol Luca Gasan | Vlad Coneschi | Alexandru Ianov
Proceedings of the Natural Legal Language Processing Workshop 2021

Recognition of named entities present in text is an important step towards information extraction and natural language understanding. This work presents a named entity recognition system for the Romanian legal domain. The system makes use of the gold annotated LegalNERo corpus. Furthermore, the system combines multiple distributional representations of words, including word embeddings trained on a large legal domain corpus. All the resources, including the corpus, model and word embeddings are open sourced. Finally, the best system is available for direct usage in the RELATE platform.

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PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors
Andrei-Marius Avram | Vasile Pais | Dan Ioan Tufis
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

EuroVoc is a multilingual thesaurus that was built for organizing the legislative documentary of the European Union institutions. It contains thousands of categories at different levels of specificity and its descriptors are targeted by legal texts in almost thirty languages. In this work we propose a unified framework for EuroVoc classification on 22 languages by fine-tuning modern Transformer-based pretrained language models. We study extensively the performance of our trained models and show that they significantly improve the results obtained by a similar tool - JEX - on the same dataset. The code and the fine-tuned models were open sourced, together with a programmatic interface that eases the process of loading the weights of a trained model and of classifying a new document.


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TermEval 2020: RACAI’s automatic term extraction system
Vasile Pais | Radu Ion
Proceedings of the 6th International Workshop on Computational Terminology

This paper describes RACAI’s automatic term extraction system, which participated in the TermEval 2020 shared task on English monolingual term extraction. We discuss the system architecture, some of the challenges that we faced as well as present our results in the English competition.

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A Processing Platform Relating Data and Tools for Romanian Language
Vasile Păiș | Radu Ion | Dan Tufiș
Proceedings of the 1st International Workshop on Language Technology Platforms

This paper presents RELATE (, a high-performance natural language platform designed for Romanian language. It is meant both for demonstration of available services, from text-span annotations to syntactic dependency trees as well as playing or automatically synthesizing Romanian words, and for the development of new annotated corpora. It also incorporates the search engines for the large COROLA reference corpus of contemporary Romanian and the Romanian wordnet. It integrates multiple text and speech processing modules and exposes their functionality through a web interface designed for the linguist researcher. It makes use of a scheduler-runner architecture, allowing processing to be distributed across multiple computing nodes. A series of input/output converters allows large corpora to be loaded, processed and exported according to user preferences.

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MWSA Task at GlobaLex 2020: RACAI’s Word Sense Alignment System using a Similarity Measurement of Dictionary Definitions
Vasile Pais | Dan Tufiș | Radu Ion
Proceedings of the 2020 Globalex Workshop on Linked Lexicography

This paper describes RACAI’s word sense alignment system, which participated in the Monolingual Word Sense Alignment shared task organized at GlobaLex 2020 workshop. We discuss the system architecture, some of the challenges that we faced as well as present our results on several of the languages available for the task.

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Collection and Annotation of the Romanian Legal Corpus
Dan Tufiș | Maria Mitrofan | Vasile Păiș | Radu Ion | Andrei Coman
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present the Romanian legislative corpus which is a valuable linguistic asset for the development of machine translation systems, especially for under-resourced languages. The knowledge that can be extracted from this resource is necessary for a deeper understanding of how law terminology is used and how it can be made more consistent. At this moment the corpus contains more than 140k documents representing the legislative body of Romania. This corpus is processed and annotated at different levels: linguistically (tokenized, lemmatized and pos-tagged), dependency parsed, chunked, named entities identified and labeled with IATE terms and EUROVOC descriptors. Each annotated document has a CONLL-U Plus format consisting in 14 columns, in addition to the standard 10-column format, four other types of annotations were added. Moreover the repository will be periodically updated as new legislative texts are published. These will be automatically collected and transmitted to the processing and annotation pipeline. The access to the corpus will be done through ELRC infrastructure.

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The MARCELL Legislative Corpus
Tamás Váradi | Svetla Koeva | Martin Yamalov | Marko Tadić | Bálint Sass | Bartłomiej Nitoń | Maciej Ogrodniczuk | Piotr Pęzik | Verginica Barbu Mititelu | Radu Ion | Elena Irimia | Maria Mitrofan | Vasile Păiș | Dan Tufiș | Radovan Garabík | Simon Krek | Andraz Repar | Matjaž Rihtar | Janez Brank
Proceedings of the Twelfth Language Resources and Evaluation Conference

This article presents the current outcomes of the MARCELL CEF Telecom project aiming to collect and deeply annotate a large comparable corpus of legal documents. The MARCELL corpus includes 7 monolingual sub-corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing the total body of respective national legislative documents. These sub-corpora are automatically sentence split, tokenized, lemmatized and morphologically and syntactically annotated. The monolingual sub-corpora are complemented by a thematically related parallel corpus (Croatian-English). The metadata and the annotations are uniformly provided for each language specific sub-corpus. Besides the standard morphosyntactic analysis plus named entity and dependency annotation, the corpus is enriched with the IATE and EUROVOC labels. The file format is CoNLL-U Plus Format, containing the ten columns specific to the CoNLL-U format and four extra columns specific to our corpora. The MARCELL corpora represents a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.