Robust 2004 is an information retrieval benchmark whose large number of
judgments per query make it a reliable evaluation dataset. In this paper, we
present mRobust04, a multilingual version of Robust04 that was translated to 8
languages using Google Translate. We also provide results of three different
multilingual retrievers on this dataset. The dataset is available at
this https URL

本文介绍了 Robust04 信息检索基准测试的多语言版本 mRobust04，并使用 Google Translate 翻译了 8 种语言。在此数据集上提供了三个不同的多语言检索器的结果，并提供数据集的链接。

mRobust04: TREC Robust 2004 基准测试的多语言版本

mRobust04: A Multilingual Version of the TREC Robust 2004 Benchmark

With the aim of promoting and understanding the multilingual version of image
search, we leverage visual object detection and propose a model with diverse
multi-head attention to learn grounded multilingual multimodal representations.
Specifically, our model attends to different types of textual semantics in two
languages and visual objects for fine-grained alignments between sentences and
images. We introduce a new objective function which explicitly encourages
attention diversity to learn an improved visual-semantic embedding space. We
evaluate our model in the German-Image and English-Image matching tasks on the
Multi30K dataset, and in the Semantic Textual Similarity task with the English
descriptions of visual content. Results show that our model yields a
significant performance gain over other methods in all of the three tasks.

本文提出了一种基于视觉物体检测和不同文本语义的多语言多模态表示的模型，采用多头注意力机制对两种语言的文本语义和视觉对象进行细粒度对齐，从而学习到更好的视觉 - 语义嵌入空间，并在多个任务上展现了比其他方法更显著的性能提升。