Fiche de FORMAL Thibault

Titre : Towards Effective, Efficient and Explainable Neural Information Retrieval
Date : 27 avril 2023
Heure: 14:00
Résumé : In this thesis, we first propose an original approach to the ad-hoc retrieval problem by learning how to represent queries and documents as sparse vectors in the vocabulary space. This results in a model that is effective, efficient, robust, and whose representations can be interpreted by design. We then propose to analyze neural ranking models from an IR perspective, by focusing on lexical match and term importance. We first show how ColBERT -- a state-of-the-art approach -- relies on such aspects despite its semantic nature. We additionally extend the findings to other models by showing how the ability to perform keyword matching is architecture-dependent and heavily influenced by the presence of query terms in the training set -- questioning the generalization capabilities of neural ranking models when it comes to exactly matching important query terms.
Lieu : Campus Jussieu Salle 304 : Tour 65-66 3ème étage (à confirmer)