Papers by Patrick Gallinari
QuestEval: Summarization Asks for Fact-based Evaluation (2021.emnlp-main)
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Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, Alex Wang, Patrick Gallinari
| Challenge: | Existing evaluation metrics for summarization evaluation are limited and do not correlate well with human judgments. |
| Approach: | They propose to extend existing evaluation metrics to include question answering models to assess whether a summary contains all relevant information in its source document. |
| Outcome: | The proposed framework significantly improves the correlation with human judgments over four evaluation dimensions. |
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction (2021.emnlp-main)
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| Challenge: | State-of-the-art NLP models adopt shallow heuristics that limit their generalization capability. |
| Approach: | They propose to use heuristics that limit their generalization capability to model lexical overlap with the training set in Named-Entity Recognition and Event or Type heuristic in Relation Extraction to test their models. |
| Outcome: | The proposed model can perform better on the two key tasks, while the retention of training relation triples. |
LOCOST: State-Space Models for Long Document Abstractive Summarization (2024.eacl-long)
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Florian Le Bronnec, Song Duong, Mathieu Ravaut, Alexandre Allauzen, Nancy Chen, Vincent Guigue, Alberto Lumbreras, Laure Soulier, Patrick Gallinari
| Challenge: | State-space models are a low-complexity alternative to transformers for text generation . however, the quadratic complexity of the input length restricts the application of large pretrained models to long texts. |
| Approach: | They propose an encoder-decoder architecture based on state-space models for conditional text generation with long context inputs. |
| Outcome: | The proposed model saves memory and memory during training and inference time while saving 50% and 87% of memory. |
Incorporating Visual Semantics into Sentence Representations within a Grounded Space (D19-1)
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| Challenge: | Language grounding is an active field aiming at enriching textual representations with visual information. |
| Approach: | They propose to transfer visual information to textual representations by learning an intermediate representation space: the grounded space. |
| Outcome: | The proposed model outperforms the previous state-of-the-art on classification and semantic relatedness tasks. |
MEXMA: Token-level objectives improve sentence representations (2025.acl-long)
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| Challenge: | Current approaches to cross-lingual sentence encoders use sentence-level objectives only. |
| Approach: | They propose a novel approach that integrates both sentence-level and token-level objectives. |
| Outcome: | The proposed approach outperforms existing CLSEs on bitext mining tasks and downstream tasks. |
Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction! (2020.emnlp-main)
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| Challenge: | Existing literature on Relation Extraction (RE) uses multiple evaluation setups to compare performance. |
| Approach: | They propose to quantify the most common comparison mistake and evaluate it leads to overestimating the final RE performance by around 5% on ACE05. |
| Outcome: | The proposed meta-analysis overestimates the final RE performance by around 5% on ACE05. |
Mixture of Languages: Improved Multilingual Encoders Through Language Grouping (2025.emnlp-main)
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João Maria Janeiro, Belen Alastruey, Francisco Massa, Maha Elbayad, Benjamin Piwowarski, Patrick Gallinari, Loic Barrault
| Challenge: | Recent work in this field relies on training transformer encoders on a large amount of multilingual data, with all parameters shared across all languages. |
| Approach: | They propose a mixture of languages strategy to pretrain largely multilingual encoders using masked language modeling. |
| Outcome: | The proposed architecture outperforms a dense counterpart, MoE models and public multilingual encoders on downstream tasks while minimizing interference. |
Context Copying Modulation: The Role of Entropy Neurons in Managing Parametric and Contextual Knowledge Conflicts (2025.findings-emnlp)
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Zineddine Tighidet, Andrea Mogini, Hedi Ben younes, Jiali Mei, Patrick Gallinari, Benjamin Piwowarski
| Challenge: | Recent work has identified in autoregressive transformer models a class of neurons that produce a significant effect on the model output entropy while having an overall moderate impact on the ranking of the predicted tokens. |
| Approach: | They identify a class of neurons that produce significant effects on the model output entropy while having an overall moderate impact on the ranking of the predicted tokens. |
| Outcome: | The entropy neurons suppressed context copying behavior in autoregressive transformer models while having moderate impact on the ranking of predicted tokens. |
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation (2021.emnlp-main)
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Clement Rebuffel, Thomas Scialom, Laure Soulier, Benjamin Piwowarski, Sylvain Lamprier, Jacopo Staiano, Geoffrey Scoutheeten, Patrick Gallinari
| Challenge: | QuestEval is a metric used in text-to-text tasks, but its adaptation to Data-to Text tasks requires multimodal Question Generation and Answering systems, which are seldom available. |
| Approach: | They propose to build synthetic multimodal corpora enabling to train multimodal components for a data-QuestEval metric. |
| Outcome: | The proposed method obtains state-of-the-art correlations with human judgment on the WebNLG and WikiBio benchmarks. |