25 citations · 50 across the 12 of their papers we have counts for
11 papers · 1 filter
Progress Ratio Embeddings: An Impatience Signal for Robust Length Control in Neural Text Generation
Ivanhoé Botcazou, Tassadit Amghar, Sylvain Lamprier +1
Modern neural language models achieve high accuracy in text generation, yet precise control over generation length remains underdeveloped. In this paper, we first investigate a rec…
Structural Deep Encoding for Table Question Answering
Raphaël Mouravieff, Benjamin Piwowarski, Sylvain Lamprier
Although Transformers-based architectures excel at processing textual information, their naive adaptation for tabular data often involves flattening the table structure. This simpl…
Which Discriminator for Cooperative Text Generation?
Antoine Chaffin, Thomas Scialom, Sylvain Lamprier +4
Language models generate texts by successively predicting probability distributions for next tokens given past ones. A growing field of interest tries to leverage external informat…
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs
Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier +2
Due to the discrete nature of words, language GANs require to be optimized from rewards provided by discriminator networks, via reinforcement learning methods. This is a much harde…
QuestEval: Summarization Asks for Fact-based Evaluation
Thomas Scialom, Paul-Alexis Dray, Patrick Gallinari +4
Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments. To alleviate this iss…
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation
Clément Rebuffel, Thomas Scialom, Laure Soulier +5
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.…