The advent of transformer-based models such as BERT has led to the rise of
neural ranking models. These models have improved the effectiveness of
retrieval systems well beyond that of lexical term matching models such as
BM25. While monolingual retrieval tasks have benefited from large-scale
training collections such as MS MARCO and advances in neural architectures,
cross-language retrieval tasks have fallen behind these advancements. This
paper introduces ColBERT-X, a generalization of the ColBERT
multi-representation dense retrieval model that uses the XLM-RoBERTa (XLM-R)
encoder to support cross-language information retrieval (CLIR). ColBERT-X can
be trained in two ways. In zero-shot training, the system is trained on the
English MS MARCO collection, relying on the XLM-R encoder for cross-language
mappings. In translate-train, the system is trained on the MS MARCO English
queries coupled with machine translations of the associated MS MARCO passages.
Results on ad hoc document ranking tasks in several languages demonstrate
substantial and statistically significant improvements of these trained dense
retrieval models over traditional lexical CLIR baselines.

本文介绍了 ColBERT-X，这是一个使用 XLM-RoBERTa 编码器的多重表示稠密检索模型，用于支持跨语言信息检索（CLIR）。在几种语言的自适应文件排名任务上表现出了在传统词汇 CLIR 基线之上的显着和统计上的显着改进。