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Match your words! A study of lexical matching in neural information retrieval
at
| Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant |
| 44th European Conference on Information Retrieval (ECIR), Stavanger, Norway, 10-14 April, 2022 |
| arXiv |
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.
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The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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