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MS-Shift: An Analysis of MS MARCO Distribution Shifts on Neural Retrieval by Lupart, Simon; Formal, Thibault; Clinchant, Stéphane is a scholarly article available to read on EtoBox.

What is MS-Shift: An Analysis of MS MARCO Distribution Shifts on Neural Retrieval about?

Pre-trained Language Models have recently emerged in Information Retrieval as providing the backbone of a new generation of neural systems that outperform traditional methods on a variety of tasks. However, it is still unclear to what extent such approaches generalize in zero-shot conditions. The recent BEIR benchmark provides partial answers to this question by comparing models on datasets and tasks that differ from the training conditions. We aim to address the same question by comparing models under more explicit distribution shifts. To this end, we build three query-based distribution shifts within MS MARCO (query-semantic, query-intent, query-length), which are used to evaluate the three main families of neural retrievers based on BERT: sparse, dense, and late-interaction -- as well as a monoBERT re-ranker. We further analyse the performance drops between the train and test query distributions. In particular, we experiment with two generalization indicators: the first one based on train/test query vocabulary overlap, and the second based on representations of a trained bi-encoder. Intuitively, those indicators verify that the further away the test set is from the train one, th

Author

Lupart, Simon; Formal, Thibault; Clinchant, Stéphane

Published

2022

Language

EN