72 citations · 148 across the 43 of their papers we have counts for
7 papers · 1 filter
Learning with Label Noise for Image Retrieval by Selecting Interactions
Sarah Ibrahimi, Arnaud Sors, Rafael Sampaio de Rezende +1
Learning with noisy labels is an active research area for image classification. However, the effect of noisy labels on image retrieval has been less studied. In this work, we propo…
A Study on Token Pruning for ColBERT
Carlos Lassance, Maroua Maachou, Joohee Park +1
The ColBERT model has recently been proposed as an effective BERT based ranker. By adopting a late interaction mechanism, a major advantage of ColBERT is that document representati…
Match Your Words! A Study of Lexical Matching in Neural Information Retrieval
Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant
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…
SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval
Thibault Formal, Carlos Lassance, Benjamin Piwowarski +1
In neural Information Retrieval (IR), ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using…
Efficient Inference for Multilingual Neural Machine Translation
Alexandre Berard, Dain Lee, Stéphane Clinchant +2
Multilingual NMT has become an attractive solution for MT deployment in production. But to match bilingual quality, it comes at the cost of larger and slower models. In this work,…
Masked Adversarial Generation for Neural Machine Translation
Badr Youbi Idrissi, Stéphane Clinchant
Attacking Neural Machine Translation models is an inherently combinatorial task on discrete sequences, solved with approximate heuristics. Most methods use the gradient to attack t…
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking
Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant
In neural Information Retrieval, ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using effic…