Search Results for author: Jacopo Staiano

Found 29 papers, 15 papers with code

Choisir le bon co-équipier pour la génération coopérative de texte (Choosing The Right Teammate For Cooperative Text Generation)

no code implementations JEP/TALN/RECITAL 2022 Antoine Chaffin, Vincent Claveau, Ewa Kijak, Sylvain Lamprier, Benjamin Piwowarski, Thomas Scialom, Jacopo Staiano

Nous évaluons leurs avantages et inconvénients, en explorant leur précision respective sur des tâches de classification, ainsi que leur impact sur la génération coopérative et leur coût de calcul, dans le cadre d’une stratégie de décodage état de l’art, basée sur une recherche arborescente de Monte-Carlo (MCTS).

Text Generation

Countering Misinformation via Emotional Response Generation

1 code implementation17 Nov 2023 Daniel Russo, Shane Peter Kaszefski-Yaschuk, Jacopo Staiano, Marco Guerini

The proliferation of misinformation on social media platforms (SMPs) poses a significant danger to public health, social cohesion and ultimately democracy.

Misinformation Response Generation

Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation

no code implementations18 Jul 2023 Federico Betti, Jacopo Staiano, Lorenzo Baraldi, Rita Cucchiara, Nicu Sebe

Research in Image Generation has recently made significant progress, particularly boosted by the introduction of Vision-Language models which are able to produce high-quality visual content based on textual inputs.

Image Generation Question Answering +1

LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization

2 code implementations26 Jan 2023 Laura Nguyen, Thomas Scialom, Benjamin Piwowarski, Jacopo Staiano

Text Summarization is a popular task and an active area of research for the Natural Language Processing community.

Text Summarization

Which Discriminator for Cooperative Text Generation?

1 code implementation25 Apr 2022 Antoine Chaffin, Thomas Scialom, Sylvain Lamprier, Jacopo Staiano, Benjamin Piwowarski, Ewa Kijak, Vincent Claveau

Language models generate texts by successively predicting probability distributions for next tokens given past ones.

Language Modelling Text Generation

Generative Cooperative Networks for Natural Language Generation

no code implementations28 Jan 2022 Sylvain Lamprier, Thomas Scialom, Antoine Chaffin, Vincent Claveau, Ewa Kijak, Jacopo Staiano, Benjamin Piwowarski

Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation.

Image Generation Text Generation

Skim-Attention: Learning to Focus via Document Layout

1 code implementation Findings (EMNLP) 2021 Laura Nguyen, Thomas Scialom, Jacopo Staiano, Benjamin Piwowarski

Motivated by human reading strategies, this paper presents Skim-Attention, a new attention mechanism that takes advantage of the structure of the document and its layout.

document understanding Language Modelling

To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs

no code implementations NeurIPS 2021 Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano

Due to the discrete nature of words, language GANs require to be optimized from rewards provided by discriminator networks, via reinforcement learning methods.

Question Generation Question-Generation +1

Rethinking Automatic Evaluation in Sentence Simplification

2 code implementations15 Apr 2021 Thomas Scialom, Louis Martin, Jacopo Staiano, Éric Villemonte de la Clergerie, Benoît Sagot

In the context of Sentence Simplification, this is particularly challenging: the task requires by nature to replace complex words with simpler ones that shares the same meaning.

Machine Translation Text Generation

Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation

2 code implementations EMNLP 2021 Clément Rebuffel, Thomas Scialom, Laure Soulier, Benjamin Piwowarski, Sylvain Lamprier, Jacopo Staiano, Geoffrey Scoutheeten, Patrick Gallinari

QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions.

Data-to-Text Generation Question Generation +1

QuestEval: Summarization Asks for Fact-based Evaluation

2 code implementations EMNLP 2021 Thomas Scialom, Paul-Alexis Dray, Patrick Gallinari, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, Alex Wang

Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments.

Question Answering

Toward Stance-based Personas for Opinionated Dialogues

no code implementations Findings of the Association for Computational Linguistics 2020 Thomas Scialom, Serra Sinem Tekiroglu, Jacopo Staiano, Marco Guerini

In the context of chit-chat dialogues it has been shown that endowing systems with a persona profile is important to produce more coherent and meaningful conversations.

Text Generation

Project PIAF: Building a Native French Question-Answering Dataset

1 code implementation LREC 2020 Rachel Keraron, Guillaume Lancrenon, Mathilde Bras, Frédéric Allary, Gilles Moyse, Thomas Scialom, Edmundo-Pavel Soriano-Morales, Jacopo Staiano

Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset.

Ranked #4 on Question Answering on FQuAD (using extra training data)

Question Answering

Discriminative Adversarial Search for Abstractive Summarization

1 code implementation ICML 2020 Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano

We introduce a novel approach for sequence decoding, Discriminative Adversarial Search (DAS), which has the desirable properties of alleviating the effects of exposure bias without requiring external metrics.

Abstractive Text Summarization Domain Adaptation

Answers Unite! Unsupervised Metrics for Reinforced Summarization Models

2 code implementations IJCNLP 2019 Thomas Scialom, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano

Abstractive summarization approaches based on Reinforcement Learning (RL) have recently been proposed to overcome classical likelihood maximization.

Abstractive Text Summarization Question Answering +2

Self-Attention Architectures for Answer-Agnostic Neural Question Generation

no code implementations ACL 2019 Thomas Scialom, Benjamin Piwowarski, Jacopo Staiano

Neural architectures based on self-attention, such as Transformers, recently attracted interest from the research community, and obtained significant improvements over the state of the art in several tasks.

Question Generation Question-Generation +1

The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective

1 code implementation13 Mar 2016 Marco De Nadai, Jacopo Staiano, Roberto Larcher, Nicu Sebe, Daniele Quercia, Bruno Lepri

This is mainly because it is hard to collect data about "city life".

Computers and Society Social and Information Networks Physics and Society

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

no code implementations23 Jun 2015 Xavier Alameda-Pineda, Jacopo Staiano, Ramanathan Subramanian, Ligia Batrinca, Elisa Ricci, Bruno Lepri, Oswald Lanz, Nicu Sebe

Studying free-standing conversational groups (FCGs) in unstructured social settings (e. g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels.

Deep Feelings: A Massive Cross-Lingual Study on the Relation between Emotions and Virality

1 code implementation16 Mar 2015 Marco Guerini, Jacopo Staiano

This article provides a comprehensive investigation on the relations between virality of news articles and the emotions they are found to evoke.

DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News

no code implementations7 May 2014 Jacopo Staiano, Marco Guerini

While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage.

Emotion Recognition General Classification +2

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