Text Summarization is a popular task and an active area of research for the Natural Language Processing community. By definition, it requires to account for long input texts, a characteristic which poses computational challenges for neural models. …
La recherche conversationnelle est une tâche qui vise à retrouver des documents à partir de la questioncourante de l'utilisateur ainsi que l'historique complet de la conversation. La plupart des méthodesantérieures sont basées sur une approche …
During past years, several frameworks for (Neural) Information Retrieval have been proposed. However, while they allow reproducing already published results, it is still very hard to re-use some parts of the learning pipelines, such as for instance …
Ces dernières années, plusieurs librairies pour la recherche d'information (neuronale) ont été proposées. Cependant, bien qu'elles permettent de reproduire des résultats déjà publiés, il est encore très difficile de réutiliser certaines parties des …
Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying …
Language models generate texts by successively predicting probability distributions for next tokens given past ones. A growing field of interest tries to leverage external information in the decoding process so that the generated texts have desired …
Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation. However, for discrete outputs such as language, optimizing GANs remains an open problem with …
Les modèles de langue génèrent des textes en prédisant successivement des distributions de probabilité pour les prochains tokens en fonction des tokens précédents. Pour générer des textes avec des propriétés souhaitées (par ex. être plus naturels, …
Neural Information Retrieval models hold the promise to replace lexical matching models, e.g. BM25, in modern search engines. While their capabilities have fully shone on in-domain datasets like MS MARCO, they have recently been challenged on …
To tackle complex IR tasks, where users cannot precisely define their needs, interaction is paramount. Both query-reformulation approaches and chatbots are limited for this type of task, since the former only learn to mimic users, while the latter …