Abstract
Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant |
44th European Conference on Information Retrieval (ECIR), Stavanger, Norway, 10-14 April, 2022 |
arXiv |
Abstract
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 out-of-domain zero-shot settings (BEIR benchmark), questioning their actual generalization capabilities compared to bag-of-words approaches. Particularly, we wonder if these shortcomings are (partly) due to the inability of neural IR models to perform lexical matching off-the-shelf. We first define how to measure the discrepancy between the lexical matching performed by the model and an “ideal” one. Based on this, we study the behavior of different state-of-the-art neural IR models, more particularly focusing on whether they are able to perform lexical matching \emph{when it’s actually useful}. Overall, we show that neural IR models fail to properly generalize term importance on out-of-domain collections or terms unseen during training.
Details on the gender equality index score 2023 (related to year 2022) for NAVER France of 81/100.
NAVER France targets are as follows:
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2023 au titre des données 2022 : 81/100
Détail des indicateurs :
Les objectifs de progression de NAVER France sont :
NAVER LABS Europe 6-8 chemin de Maupertuis 38240 Meylan France Contact
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