72 citations · 148 across the 43 of their papers we have counts for
10 papers · 1 filter
Benchmarking Middle-Trained Language Models for Neural Search
Hervé Déjean, Stéphane Clinchant, Carlos Lassance +2
Middle training methods aim to bridge the gap between the Masked Language Model (MLM) pre-training and the final finetuning for retrieval. Recent models such as CoCondenser, RetroM…
The tale of two MS MARCO -- and their unfair comparisons
Carlos Lassance, Stéphane Clinchant
The MS MARCO-passage dataset has been the main large-scale dataset open to the IR community and it has fostered successfully the development of novel neural retrieval models over t…
A Static Pruning Study on Sparse Neural Retrievers
Carlos Lassance, Simon Lupart, Hervé Dejean +2
Sparse neural retrievers, such as DeepImpact, uniCOIL and SPLADE, have been introduced recently as an efficient and effective way to perform retrieval with inverted indexes. They a…
AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation
Jheng-Hong Yang, Carlos Lassance, Rafael Sampaio de Rezende +4
This paper presents the AToMiC (Authoring Tools for Multimedia Content) dataset, designed to advance research in image/text cross-modal retrieval. While vision-language pretrained…
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters
Vaishali Pal, Carlos Lassance, Hervé Déjean +1
Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and…
Naver Labs Europe (SPLADE) @ TREC NeuCLIR 2022
Carlos Lassance, Stéphane Clinchant
This paper describes our participation in the 2022 TREC NeuCLIR challenge. We submitted runs to two out of the three languages (Farsi and Russian), with a focus on first-stage rank…
Naver Labs Europe (SPLADE) @ TREC Deep Learning 2022
Carlos Lassance, Stéphane Clinchant
This paper describes our participation to the 2022 TREC Deep Learning challenge. We submitted runs to all four tasks, with a focus on the full retrieval passage task. The strategy…
Query Performance Prediction for Neural IR: Are We There Yet?
Guglielmo Faggioli, Thibault Formal, Stefano Marchesin +3
Evaluation in Information Retrieval relies on post-hoc empirical procedures, which are time-consuming and expensive operations. To alleviate this, Query Performance Prediction (QPP…
A Study on FGSM Adversarial Training for Neural Retrieval
Simon Lupart, Stéphane Clinchant
Neural retrieval models have acquired significant effectiveness gains over the last few years compared to term-based methods. Nevertheless, those models may be brittle when faced t…
An Experimental Study on Pretraining Transformers from Scratch for IR
Carlos Lassance, Hervé Déjean, Stéphane Clinchant
Finetuning Pretrained Language Models (PLM) for IR has been de facto the standard practice since their breakthrough effectiveness few years ago. But, is this approach well understo…