Papers by Paolo Rosso
FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Bias (2022.lrec-1)
Copied to clipboard
| Challenge: | Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. |
| Approach: | They propose a new reddit dataset for fake news spreader analysis, called FACTOID, which tracks political discussions on Reddit since the beginning of 2020. |
| Outcome: | The proposed dataset contains over 4K users with 3.4M posts and includes their credibility level (very low to very high) and political bias strength (extreme right to extreme left). |
“Stupid robot, I want to speak to a human!” User Frustration Detection in Task-Oriented Dialog Systems (2025.coling-industry)
Copied to clipboard
Mireia Hernandez Caralt, Ivan Sekulic, Filip Carevic, Nghia Khau, Diana Nicoleta Popa, Bruna Guedes, Victor Guimaraes, Zeyu Yang, Andre Manso, Meghana Reddy, Paolo Rosso, Roland Mathis
| Challenge: | Detecting user frustration in task-oriented dialog systems is imperative for maintaining overall user satisfaction, engagement and retention. |
| Approach: | They compare out-of-the-box methods for user frustration detection with open-source methods . they find an LLM-based approach is promising, as it captures both emotion and dialog breakdowns . |
| Outcome: | The proposed method outperforms open-source methods in detecting user frustration in a TOD system. |
FakeFlow: Fake News Detection by Modeling the Flow of Affective Information (2021.eacl-main)
Copied to clipboard
| Challenge: | In short news articles, authors add exaggerations or fabricate events to manipulate readers' emotions. |
| Approach: | They propose to model the flow of affective information in fake news articles using a neural architecture and combine topic and affective data extracted from text. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four real-world datasets and shows that it can capture the flow of affective information in fake news articles. |
Definitions Matter: Guiding GPT for Multi-label Classification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent success of Large Language Models (LLMs) is due to their superior performance on various tasks such as text generation, summarization, question answering, and inductive reasoning. |
| Approach: | They propose to generate definitions from examples and use them for zero-shot classification and to investigate how an LLM makes use of the definitions. |
| Outcome: | The proposed method improves the definitions of class labels and improves their understanding of the definition. |
Multi-Aspect Transfer Learning for Detecting Low Resource Mental Disorders on Social Media (2022.lrec-1)
Copied to clipboard
| Challenge: | Mental disorders are an important and pervasive public health issue. |
| Approach: | They propose to use linguistic features to improve mental disorder detection . they propose to apply multi-aspect transfer learning to detecting disorders from social media . |
| Outcome: | The proposed methods can be used to improve mental disorder detection in the context of data scarcity and understanding the overlapping symptoms between disorders. |
Marking Irony Activators in a Universal Dependencies Treebank: The Case of an Italian Twitter Corpus (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing annotations for irony are difficult, and the recognition of it is difficult due to its polarity. |
| Approach: | They propose a fine-grained annotation scheme centered on irony that highlights the tokens responsible for its activation and their morpho-syntactic features. |
| Outcome: | The proposed scheme highlights the tokens responsible for irony activation and their morpho-syntactic features. |
CATS: A Tool for Customized Alignment of Text Simplification Corpora (L18-1)
Copied to clipboard
| Challenge: | Existing corpora of original sentences and their manual simplifications are very scarce and small in size, hindering automated text simplification systems. |
| Approach: | They propose a language-independent tool for sentence alignment from parallel/comparable TS resources. |
| Outcome: | The proposed tool performs well on English and Spanish corpora and compares sentences based on their semantic overlap. |
Efficient Out-of-Scope Detection in Dialogue Systems via Uncertainty-Driven LLM Routing (2025.acl-industry)
Copied to clipboard
| Challenge: | Out-of-scope (OOS) intent detection is critical in task-oriented dialogue systems . without effective OOS detection, such inputs could lead to incorrect responses, reduced user trust, and eventual system failures. |
| Approach: | They propose a modular framework that combines uncertainty modeling with fine-tuned large language models (LLMs) their method yields state-of-the-art results on key OOS detection benchmarks . |
| Outcome: | The proposed framework yields state-of-the-art results on key OOS detection benchmarks including real-world OOS data. |
Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models (2022.naacl-main)
Copied to clipboard
| Challenge: | speculative trading of highly volatile assets such as cryptocurrencies and meme stocks presents a new challenge in the financial realm. |
| Approach: | They propose a multi-span bubble detection task based on social media hype and a set of sequence-to-sequence hyperbolic models . they use data from 9 exchanges over five years to test their models based upon the power-law dynamics of cryptocurrencies and user behavior on social networks. |
| Outcome: | The proposed model is able to detect bubbles on a set of reddit and twitter posts spanning over two million tweets over five years . |
Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for few-shot cross-lingual transfer learning are limited in target languages due to the scarcity of resources. |
| Approach: | They propose a method which interpolates pairs of instances based on the angle of their representations and propose augmentation methods to enhance few-shot cross-lingual abusive language detection. |
| Outcome: | The proposed method improves few-shot cross-lingual abusive language detection in seven languages typologically distinct from English and three different domains. |
PyRater: A Python Toolkit for Annotation Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | PyRater is an open-source Python toolkit for analysing corpora annotations. |
| Approach: | They propose to use PyRater to analyse corpora annotations. |
| Outcome: | The proposed model can be used to identify the best annotations and retrieve the gold standard. |
RoCode: A Dataset for Measuring Code Intelligence from Problem Definitions in Romanian (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models are capable of solving tasks in natural language, but most tests assume they are written in English. |
| Approach: | They propose to use a dataset to measure the generalization power of large language models in a language other than English to evaluate their code intelligence. |
| Outcome: | The proposed dataset provides a benchmark for evaluating the code intelligence of language models trained on Romanian / multilingual text and a fine-tuning set for pretrained Romanian models. |
Zero-Shot Data Maps. Efficient Dataset Cartography Without Model Training (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to diagnose large annotated datasets require the fitting of a strong model to the dataset. |
| Approach: | They propose a new approach to compute confidence and variability over an ensemble of zero-shot models constructed with different but semantically equivalent label descriptions. |
| Outcome: | The proposed method can be used to diagnose large annotated datasets with accuracy up to 14x faster than the current method. |
MemeWeaver: Inter-Meme Graph Reasoning for Sexism and Misogyny Detection (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to detect hate speech on social media are limited by heuristic graph construction, shallow modality fusion, and instance-level reasoning. |
| Approach: | They propose a multimodal framework for detecting sexism and misogyny using a graph reasoning mechanism that can be used to train multiple visual-textual fusion strategies. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on MAMI and EXIST benchmarks while achieving faster training convergence. |
Multilingual Irony Detection with Dependency Syntax and Neural Models (2020.coling-main)
Copied to clipboard
Alessandra Teresa Cignarella, Valerio Basile, Manuela Sanguinetti, Cristina Bosco, Paolo Rosso, Farah Benamara
| Challenge: | Several semantic and syntactic devices can be used to express irony, causing the incongruity, determine the clash and play the role of irony triggers within a text. |
| Approach: | They propose to exploit linguistic resources where syntax is annotated according to the Universal Dependencies scheme. |
| Outcome: | The proposed method exploits linguistic resources where syntax is annotated according to the Universal Dependencies scheme. |
Unsupervised Embeddings with Graph Auto-Encoders for Multi-domain and Multilingual Hate Speech Detection (2022.lrec-1)
Copied to clipboard
| Challenge: | Hate speech detection is a challenging task, since hate messages are often expressed in subtle ways and with characteristics that may vary depending on the author. |
| Approach: | They propose an unsupervised approach to learn embeddings for hate speech detection using Graph Auto-Encoders (GAE) they represent texts as nodes of a graph and use transformer layer and convolutional layer to encode them in low-dimensional space. |
| Outcome: | The proposed method shows competitive results on small datasets. |