While billions of non-English speaking users rely on search engines every
day, the problem of ad-hoc information retrieval is rarely studied for
non-English languages. This is primarily due to a lack of data set that are
suitable to train ranking algorithms. In this paper, we tackle the lack of data
by leveraging pre-trained multilingual language models to transfer a retrieval
system trained on English collections to non-English queries and documents. Our
model is evaluated in a zero-shot setting, meaning that we use them to predict
relevance scores for query-document pairs in languages never seen during
training. Our results show that the proposed approach can significantly
outperform unsupervised retrieval techniques for Arabic, Chinese Mandarin, and
Spanish. We also show that augmenting the English training collection with some
examples from the target language can sometimes improve performance.

本文旨在解决搜索引擎在非英语语言中的信息检索问题，并提出了基于预训练的多语言语言模型的检索系统，能有效提高阿拉伯语，普通话和西班牙语的检索准确性，并通过在英语数据集中增加目标语言样例来改进性能。

教新狗古技：利用零 - shot 学习复苏多语言检索

Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using  Zero-shot Learning

Over the past years, deep learning methods allowed for new state-of-the-art
results in ad-hoc information retrieval. However such methods usually require
large amounts of annotated data to be effective. Since most standard ad-hoc
information retrieval datasets publicly available for academic research (e.g.
Robust04, ClueWeb09) have at most 250 annotated queries, the recent deep
learning models for information retrieval perform poorly on these datasets.
These models (e.g. DUET, Conv-KNRM) are trained and evaluated on data collected
from commercial search engines not publicly available for academic research
which is a problem for reproducibility and the advancement of research. In this
paper, we propose WIKIR: an open-source toolkit to automatically build
large-scale English information retrieval datasets based on Wikipedia. WIKIR is
publicly available on GitHub. We also provide wikIR78k and wikIRS78k: two
large-scale publicly available datasets that both contain 78,628 queries and
3,060,191 (query, relevant documents) pairs.

本文提出了一种基于 Wikipedia 的开源工具 WIKIR 来自动构建大规模英文信息检索数据集，提供了 wikIR78k 和 wikIRS78k 两种共 78628 个查询和 3060191 个 (query,relevant documents) 匹配数据的公开数据集，以解决当前深度学习方法在小数据集上表现不佳并难以进行学术研究问题。