Search Results for author: Răzvan-Alexandru Smădu

Found 7 papers, 0 papers with code

UPB at SemEval-2021 Task 7: Adversarial Multi-Task Learning for Detecting and Rating Humor and Offense

no code implementations SEMEVAL 2021 Răzvan-Alexandru Smădu, Dumitru-Clementin Cercel, Mihai Dascalu

Detecting humor is a challenging task since words might share multiple valences and, depending on the context, the same words can be even used in offensive expressions.

Multi-Task Learning text-classification +1

Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification

no code implementations ACL 2022 George-Eduard Zaharia, Răzvan-Alexandru Smădu, Dumitru-Clementin Cercel, Mihai Dascalu

Our model obtains a boost of up to 2. 42% in terms of Pearson Correlation Coefficients in contrast to vanilla training techniques, when considering the CompLex from the Lexical Complexity Prediction 2021 dataset.

Complex Word Identification Domain Adaptation +2

TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)

no code implementations30 Dec 2022 Răzvan-Alexandru Smădu, George-Eduard Zaharia, Andrei-Marius Avram, Dumitru-Clementin Cercel, Mihai Dascalu, Florin Pop

Keyphrase identification and classification is a Natural Language Processing and Information Retrieval task that involves extracting relevant groups of words from a given text related to the main topic.

Domain Adaptation Information Retrieval +3

Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis

no code implementations13 Jun 2023 Sebastian-Vasile Echim, Răzvan-Alexandru Smădu, Andrei-Marius Avram, Dumitru-Clementin Cercel, Florin Pop

Satire detection and sentiment analysis are intensively explored natural language processing (NLP) tasks that study the identification of the satirical tone from texts and extracting sentiments in relationship with their targets.

Satire Detection Sentiment Analysis

Towards Improving the Performance of Pre-Trained Speech Models for Low-Resource Languages Through Lateral Inhibition

no code implementations30 Jun 2023 Andrei-Marius Avram, Răzvan-Alexandru Smădu, Vasile Păiş, Dumitru-Clementin Cercel, Radu Ion, Dan Tufiş

With the rise of bidirectional encoder representations from Transformer models in natural language processing, the speech community has adopted some of their development methodologies.

From Fake to Hyperpartisan News Detection Using Domain Adaptation

no code implementations4 Aug 2023 Răzvan-Alexandru Smădu, Sebastian-Vasile Echim, Dumitru-Clementin Cercel, Iuliana Marin, Florin Pop

In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection.

Clustering Contrastive Learning +5

End-to-End Lip Reading in Romanian with Cross-Lingual Domain Adaptation and Lateral Inhibition

no code implementations7 Oct 2023 Emilian-Claudiu Mănescu, Răzvan-Alexandru Smădu, Andrei-Marius Avram, Dumitru-Clementin Cercel, Florin Pop

Lip reading or visual speech recognition has gained significant attention in recent years, particularly because of hardware development and innovations in computer vision.

Domain Adaptation Lip Reading +2

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