Papers by Géraldine Damnati
DivMerge: A divergence-based model merging method for multi-tasking (2026.eacl-long)
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| Challenge: | Existing methods for multitask learning struggle with interference between tasks, especially as the number of tasks grows. |
| Approach: | They propose a reference-free method that minimizes the divergence between models' outputs and those of the merged model, automatically balancing task importance. |
| Outcome: | The proposed method outperforms existing methods on classification and generative tasks and remains robust when scaling to more tasks. |
Question Generation and Answering for exploring Digital Humanities collections (2022.lrec-1)
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| Challenge: | Recent advances in representation learning of text have achieved impressive results on benchmark Natural Language Understanding (NLU) tasks. |
| Approach: | They propose a question answering paradigm that uses a BART Transformer based generative model to generate question data. |
| Outcome: | The proposed approach is validated on a new corpus of digitized archive collections of a French Social Science journal. |
Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text (L18-1)
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Géraldine Damnati, Jeremy Auguste, Alexis Nasr, Delphine Charlet, Johannes Heinecke, Frédéric Béchet
| Challenge: | a new approach to POS tagging noisy user generated text is proposed . word embeddings are trained on a noisy corpus to address both normalization and POS. |
| Approach: | They propose to use word embeddings to normalize text before tagging it, while a gated neural network based tagger handles the remaining errors. |
| Outcome: | The proposed approach normalizes some errors before tagging, while a gated neural network handles the remaining errors. |
A linguistically-motivated evaluation methodology for unraveling model’s abilities in reading comprehension tasks (2024.emnlp-main)
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| Challenge: | Existing models fail for linguistic characteristics of input examples, despite the impressive quantity of scientific studies dedicated to them, the capabilities, limitations, and risks of these models remain largely unknown. |
| Approach: | They propose to use semantic frame annotation to characterize examples by a small number of complexity factors to account for model’s difficulty. |
| Outcome: | The proposed evaluation methodology is based on the intuition that certain examples consistently yield lower scores regardless of model size or architecture. |
Robust Semantic Parsing with Adversarial Learning for Domain Generalization (N19-2)
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| Challenge: | Using adversarial learning to train models on a higher level of abstraction to increase their robustness to lexical and stylistic variations is crucial for the integration of Semantic Parsing technologies in real applications. |
| Approach: | They propose to perform Semantic Parsing with a domain classification adversarial task and an unsupervised domain discovery approach that yields equivalent improvements. |
| Outcome: | The proposed approach improves on a French corpus of encyclopedic documents annotated with FrameNet and an unsupervised domain discovery approach yields equivalent improvements. |
FrNewsLink : a corpus linking TV Broadcast News Segments and Press Articles (L18-1)
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Nathalie Camelin, Géraldine Damnati, Abdessalam Bouchekif, Anais Landeau, Delphine Charlet, Yannick Estève
| Challenge: | a corpus of TV Broadcast News resources is proposed to address several applicative tasks. |
| Approach: | They propose to use a corpus to address several applicative tasks that are made public . they propose to gather TVBN shows and press articles and use them to study semantic similarity . |
| Outcome: | The proposed corpus is based on 112 TVBN shows and press articles . it allows to study semantic similarity and multimedia News linking . |
Statistical Deficiency for Task Inclusion Estimation (2025.acl-long)
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Loïc Fosse, Frederic Bechet, Benoit Favre, Géraldine Damnati, Gwénolé Lecorvé, Maxime Darrin, Philippe Formont, Pablo Piantanida
| Challenge: | Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. |
| Approach: | They propose a theoretically grounded setup to define the notion of task and compute the inclusion between two tasks from a statistical deficiency point of view. |
| Outcome: | The proposed model estimates the degree of inclusion between tasks on synthetic data and reconstructs the classic NLP pipeline. |