Papers by Véronique Moriceau
CrisisTS: Coupling Social Media Textual Data and Meteorological Time Series for Urgency Classification (2025.acl-long)
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| Challenge: | Existing studies on fusion of texts and tabular-based time series to improve performance of NLP applications have focused on coupling texts with tabular time series. |
| Approach: | They propose a multimodal and multilingual dataset for urgency classification that allows for temporal and location alignment even in the absence of location mention in the text. |
| Outcome: | The proposed dataset outperforms text-only models in many applications while ensuring model generalizability. |
Image and Text: Fighting the same Battle? Super Resolution Learning for Imbalanced Text Classification (2023.findings-emnlp)
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| Challenge: | Using high-resolution images to overcome the problem of low resolution has never been used in NLP. |
| Approach: | They propose a super-resolution learning method that uses high-res images to overcome the problem of low resolution images. |
| Outcome: | The proposed method is efficient when compared to state-of-the-art methods on several benchmarks datasets in two languages. |
Speech acts and Communicative Intentions for Urgency Detection (2022.starsem-1)
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| Challenge: | Existing approaches to detect speech acts (SA) in synchronous and asynchronous dialogues have been proposed to capture communicative intentions on the part of the speaker. |
| Approach: | They propose to annotate tweets with urgency and SA and develop deep learning architectures to inject it into urgency detection. |
| Outcome: | The proposed dataset annotated for urgency and SA improves information type detection in an out-of-type configuration where models are evaluated in unseen event types during training. |
Humans Need Context, What about Machines? Investigating Conversational Context in Abusive Language Detection (2024.lrec-main)
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| Challenge: | In this paper, we examine the role of conversational context in abusive language detection . prior studies have ignored the contextual nature of abusive language, ignoring this aspect . toxicity, hate speech, harmful stereotypes are among the forms of harmful language . |
| Approach: | They propose to use conversational context to analyze abusive language detection using two methods . they use "abusive language" as an umbrella term to refer to various forms of harmful language . |
| Outcome: | The proposed approach is based on two datasets in English and a new dataset of French tweets annotated for hate speech and stereotypes. |
What Did You Learn To Hate? A Topic-Oriented Analysis of Generalization in Hate Speech Detection (2023.eacl-main)
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| Challenge: | Hate speech detection datasets often use different annotation guidelines, resulting in inconsistencies . authors propose a topic-oriented approach to study generalization across popular hate speech datasets . |
| Approach: | They propose a topic-oriented approach to study generalization across popular hate speech datasets . they compare Transformer-based models in capturing topic-generic and topic-specific knowledge . |
| Outcome: | The proposed approach improves the reliability of hate speech detection on social media platforms. |
An Annotated Corpus for Sexism Detection in French Tweets (2020.lrec-1)
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Patricia Chiril, Véronique Moriceau, Farah Benamara, Alda Mari, Gloria Origgi, Marlène Coulomb-Gully
| Challenge: | Social media networks allow users to share opinions and sentiments, which can cause a large spreading of hatred or abusive messages. |
| Approach: | They propose to annotate 12,000 tweets with a sexism detection scheme in France . they propose to use deep learning to detect if a message with sexist content is really s. |
| Outcome: | The proposed scheme detects sexist content and identifies if it is really sexism . the proposed scheme is the first of its kind in the u.s. |
Give me your Intentions, I’ll Predict our Actions: A Two-level Classification of Speech Acts for Crisis Management in Social Media (2022.lrec-1)
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| Challenge: | Using social networks, social media is a vital tool for emergency management and social media has been used to generate valuable information in crisis situations. |
| Approach: | They propose to measure for the first time the role of SA on urgency detection in tweets . they propose to use a two-layer annotation scheme to annotate tweets for both SA and urgency . |
| Outcome: | The proposed scheme combines two-layer annotation scheme and deep learning experiments to detect SA in a crisis corpus. |
Automatic Detection of Stigmatizing Uses of Psychiatric Terms on Twitter (2022.lrec-1)
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| Challenge: | Psychiatry and people suffering from mental disorders have often been given a pejorative label that induces social rejection. |
| Approach: | They propose to use deep learning to detect polarity and type of use in tweets . they propose to combine polarization detection with typeof use detection to improve polarities . |
| Outcome: | The proposed models can detect the polarity of a tweet and the types of use on a dataset that is not yet available. |
A Multilingual Dataset of Racial Stereotypes in Social Media Conversational Threads (2023.findings-eacl)
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Tom Bourgeade, Alessandra Teresa Cignarella, Simona Frenda, Mario Laurent, Wolfgang Schmeisser-Nieto, Farah Benamara, Cristina Bosco, Véronique Moriceau, Viviana Patti, Mariona Taulé
| Challenge: | a new corpus-based study addresses racial stereotypes in social media conversations . a multilingual corpus of rhs is used to investigate how they are spread . |
| Approach: | They propose a corpus-based method for multilingual racial stereotype identification in social media conversational threads. |
| Outcome: | The proposed method sheds light on how racial hoaxes are spread and allows identification of negative stereotypes that reinforce them. |
“Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification? (2021.findings-emnlp)
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| Challenge: | a new study examines the impact of gender stereotype detection on sexism classification . GS is defined as "pictures in our heads" and is used to describe social group members . |
| Approach: | They propose to use tweets as a dataset to detect sexist hate speech . they propose a method for data augmentation based on sentence similarity with external datasets . |
| Outcome: | The proposed method detects sexist hate speech in tweets and then uses it for sexism classification. |
He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist (2020.acl-main)
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Patricia Chiril, Véronique Moriceau, Farah Benamara, Alda Mari, Gloria Origgi, Marlène Coulomb-Gully
| Challenge: | Sexism is prejudice or discrimination based on a person's gender. |
| Approach: | They propose to use a French dataset annotated for sexism detection to characterize sexist content and to train deep learning experiments on tweets. |
| Outcome: | The proposed dataset is the first to be used for sexism detection in France and constitutes a first step towards offensive content moderation. |