Papers by Romain Meunier

2 papers
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.

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