Search Results for author: Thomas Scialom

Found 18 papers, 11 papers with code

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

1 code implementation6 Dec 2021 Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Srivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein, Jinho D. Choi, Eduard Hovy, Ondrej Dusek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K. Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxime Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Muennighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicolas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos P. H. S., Ananya B. Sai, Robin M. Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib N. Shamsi, Xudong Shen, Haoyue Shi, Yiwen Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, KV Aditya Srivatsa, Tony Sun, Mukund Varma T, A Tabassum, Fiona Anting Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Gloria Wang, Zijie J. Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyi Wu, Witold Wydmański, Tianbao Xie, Usama Yaseen, M. Yee, Jing Zhang, Yue Zhang

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.

Data Augmentation

BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation

1 code implementation18 Oct 2021 Thomas Scialom, Felix Hill

There is currently no simple, unified way to compare, analyse or evaluate metrics across a representative set of tasks.

Informativeness 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.

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

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

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

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

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 Word Embeddings

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