1.1k citations · 1.5k across the 13 of their papers we have counts for
16 papers
Domain Adaptation for Dense Retrieval through Self-Supervision by Pseudo-Relevance Labeling
Minghan Li, Eric Gaussier
Although neural information retrieval has witnessed great improvements, recent works showed that the generalization ability of dense retrieval models on target domains with differe…
Where Do You Want To Invest? Predicting Startup Funding From Freely, Publicly Available Web Information
Mariia Garkavenko, Eric Gaussier, Hamid Mirisaee +2
We consider in this paper the problem of predicting the ability of a startup to attract investments using freely, publicly available data. Information about startups on the web usu…
The Power of Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval
Minghan Li, Diana Nicoleta Popa, Johan Chagnon +2
On a wide range of natural language processing and information retrieval tasks, transformer-based models, particularly pre-trained language models like BERT, have demonstrated trem…
SmoothI: Smooth Rank Indicators for Differentiable IR Metrics
Thibaut Thonet, Yagmur Gizem Cinar, Eric Gaussier +2
Information retrieval (IR) systems traditionally aim to maximize metrics built on rankings, such as precision or NDCG. However, the non-differentiability of the ranking operation p…
Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees
Myriam Tami, Marianne Clausel, Emilie Devijver +4
Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these…
Char2char Generation with Reranking for the E2E NLG Challenge
Shubham Agarwal, Marc Dymetman, Eric Gaussier
This paper describes our submission to the E2E NLG Challenge. Recently, neural seq2seq approaches have become mainstream in NLG, often resorting to pre- (respectively post-) proces…