Papers by Radu Tudor Ionescu
SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles (2021.acl-short)
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| Challenge: | a corpus for satire detection in Romanian news is based on satirical reporting . the goal is to ridicule public figures, politics or contemporary events . |
| Approach: | They propose a corpus for satire detection in Romanian news . they gather 55,608 public news articles from multiple real and satirical sources . |
| Outcome: | The proposed corpus is one of the largest corpora for satire detection regardless of language . it is the only one for the Romanian language, and the results show that it is low on the machine level compared to human level . |
Task-Informed Anti-Curriculum by Masking Improves Downstream Performance on Text (2025.findings-acl)
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| Challenge: | Masked language modeling is widely adopted, but the process of selecting tokens for masking is random and the percentage of masked tokens is typically fixed for the entire training process. |
| Approach: | They propose to adjust the masking ratio based on a task-informed anti-curriculum learning scheme to mask useful and harmful tokens. |
| Outcome: | The proposed approach improves the ability of the model to focus on key task-relevant features, contributing to statistically significant performance gains across tasks. |
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set (D18-1)
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| Challenge: | Recent studies have demonstrated remarkable performance in text classification tasks such as Arabic dialect identification. |
| Approach: | They propose two approaches to improve string kernels' accuracy in Arabic and English . first approach interprets pairwise string kernel similarities between training and test sets as features . second approach adapts to training set and adds test samples for another round of training . |
| Outcome: | The proposed methods improve English polarity classification and Arabic dialect identification. |
CLewR: Curriculum Learning with Restarts for Machine Translation Preference Learning (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated competitive performance in zero-shot multilingual machine translation (MT). |
| Approach: | They propose a curriculum learning strategy with restarts which reiterates easy-to-hard curriculum multiple times during training to effectively mitigate catastrophic forgetting of easy examples. |
| Outcome: | The proposed model replicates easy-to-hard curriculum multiple times during training to mitigate catastrophic forgetting of easy examples. |
Automated essay scoring with string kernels and word embeddings (P18-2)
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| Challenge: | Existing approaches to automatic essay scoring use low-level character n-gram features. |
| Approach: | They propose to combine string kernels and word embeddings for automatic essay scoring. |
| Outcome: | The proposed method outperforms state-of-the-art deep learning methods in Arabic dialect identification and native language identification tasks. |
Rethinking the Authorship Verification Experimental Setups (2022.emnlp-main)
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| Challenge: | Identifying the author of a text is one of the most versatile NLP tasks, with applications ranging from plagiarism detection to forensics and monitoring the activity of cyber-criminals. |
| Approach: | They propose five new public splits over the PAN dataset to isolate and identify biases related to the text topic and to the author’s writing style. |
| Outcome: | The proposed models are competitive with state-of-the-art methods and generalize better on dark reddit datasets. |
XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark (2026.findings-eacl)
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Ioan-Paul Ciobanu, Andrei-Iulian Hîji, Nicolae Catalin Ristea, Paul Irofti, Cristian Rusu, Radu Tudor Ionescu
| Challenge: | Recent advances in audio generation led to an increasing number of deepfakes . however, these methods are typically tested in an in-domain setup . |
| Approach: | They propose a large-scale cross-domain audio deepfake benchmark comprising 668.8 hours of real and deepfak speech. |
| Outcome: | The proposed benchmark compares audio deepfake detectors with existing methods in the wild . the results show that the proposed methods perform better in different languages than existing methods . |
Text Classification Under Class Distribution Shift: A Survey (2026.eacl-long)
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| Challenge: | ML models assume that training and test data are sampled from the same distribution, but in daily practice, this assumption is often broken. |
| Approach: | They survey articles studying open-set text classification to understand the distribution shifts and mitigation approaches for each problem setup. |
| Outcome: | The proposed methods can solve problems caused by the shifting class distribution in open-set text classification and related tasks. |
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation (N19-1)
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| Challenge: | a novel word embedding representation for text documents is proposed . the method is based on the Vector of Locally-Aggregated Descriptors used for image representation . |
| Approach: | They propose a novel representation for text documents based on aggregating word embedding vectors into document embeddables. |
| Outcome: | The proposed representation improves on the Movie Review data set and is 10% better than the state-of-the-art representation. |
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set (2021.eacl-main)
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| Challenge: | Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. |
| Approach: | They introduce a Large Romanian Sentiment Data Set which is composed of 15,000 positive and negative reviews collected from the largest Romanian e-commerce platform. |
| Outcome: | The proposed data set is composed of 15,000 positive and negative reviews from the largest Romanian e-commerce platform. |
MOROCO: The Moldavian and Romanian Dialectal Corpus (P19-1)
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| Challenge: | Using the MOldavian and ROmanian Dialectal COrpus, we perform empirical studies on dialect identification tasks. |
| Approach: | They introduce the MOldavian and ROmanian Dialectal COrpus corpus which contains 33564 samples of text collected from the news domain. |
| Outcome: | The proposed model is based on a shallow and deep approach to discriminate between two different languages. |