Papers by Christophe Gravier
T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples (L18-1)
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Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, Elena Simperl
| Challenge: | Existing datasets that provide alignments between natural language and knowledge bases (KB) triples are limited in size, lack coverage and are of unreported quality. |
| Approach: | They propose to build a large scale dataset of alignments between Wikipedia abstracts and Wikidata triples that is two orders of magnitude larger than the largest available alignments dataset. |
| Outcome: | The proposed dataset is two orders of magnitude larger than the largest available dataset and covers 2.5 times more predicates. |
A Neural Few-Shot Text Classification Reality Check (2021.eacl-main)
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| Challenge: | Modern few-shot text classification models struggle when the amount of annotated data is scarce. |
| Approach: | They compare neural few-shot classification models with NLP and computer vision models with transformers to test their performance. |
| Outcome: | The proposed models perform almost equally on ARSC dataset, but not on the intent detection task. |
HISTOIRESMORALES: A French Dataset for Assessing Moral Alignment (2025.naacl-long)
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Thibaud Leteno, Irina Proskurina, Antoine Gourru, Julien Velcin, Charlotte Laclau, Guillaume Metzler, Christophe Gravier
| Challenge: | HistoiresMorales is a dataset based on moralStories in French . it is based upon annotations of moral values within the dataset . |
| Approach: | They propose a dataset in French that aims to align language models with moral values . they use annotations to ensure their alignment with French norms . |
| Outcome: | The proposed dataset guarantees grammatical accuracy and adaptation to the French cultural context. |
PROTAUGMENT: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning (2021.acl-long)
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| Challenge: | Recent research considers few-shot intent detection as a meta-learning problem because of labeled data scarcity and the number of classes involved. |
| Approach: | They propose a meta-learning algorithm for short texts classification that limits overfitting on the bias introduced by the few-shots classification objective at each episode. |
| Outcome: | The proposed algorithm limits overfitting on the bias introduced by the few-shots classification objective at each episode. |
Few-shot Pseudo-Labeling for Intent Detection (2020.coling-main)
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| Challenge: | Existing methods for labeling intents are expensive and time-consuming. |
| Approach: | They propose a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances. |
| Outcome: | The proposed method performs better on multiple intent detection datasets and is stronger than existing methods. |
Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types (N18-1)
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| Challenge: | Existing factoid question answering systems rely on annotated datasets such as SimpleQuestions to generate questions from knowledge graphs. |
| Approach: | They propose a neural model that generates questions from knowledge graphs triples in a “zero-shot” setup. |
| Outcome: | The proposed model outperforms state-of-the-art on this task. |
Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata (N18-2)
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Lucie-Aimée Kaffee, Hady Elsahar, Pavlos Vougiouklis, Christophe Gravier, Frédérique Laforest, Jonathon Hare, Elena Simperl
| Challenge: | Existing Wikipedia content is unevenly distributed among 287 languages . authors propose a neural network architecture that generates textual summaries from Wikidata triples . |
| Approach: | They propose an automated approach to generate Wikipedia summaries from Wikidata triples using structured data. |
| Outcome: | The proposed approach is tested on Arabic and Esperanto languages with limited editors and content in the most under-resourced Wikipedias. |
Fair Text Classification with Wasserstein Independence (2023.emnlp-main)
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| Challenge: | a novel method for mitigating group fairness in text classification remains an open challenge . a recent paper focuses on the problem of group fair in the domain of natural language processing . |
| Approach: | They propose a method for mitigating group fairness in neural text classification . they use adversarial training to induce Wasserstein independence between representations . |
| Outcome: | The proposed method exhibits a comparable or better fairness-accuracy trade-off compared to existing methods. |