零样本多语言问答重排序，无需标注数据即可提升跨语言检索效果
Multilingual Open QA on the MIA Shared Task
- 用预训练多语言模型生成问题，重评分跨语言检索结果
- 完全零样本设置下，可显著提升低资源语言的问答准确率
- 适用于任意稀疏检索方法，特别适合缺乏标注数据的语言
跨语言信息检索（CLIR）可在不同语言间查找相关文本，例如用英语（高资源语言）或泰卢固语（低资源语言）查询，即使查询语言本身是低资源语言。本文旨在构建适用于该受限但重要的场景的CLIR模型，不依赖任何额外监督或标注数据，从而有效支持低资源语言。我们提出一种简单有效的重排序方法，用于改进开放域问答中的段落检索。该重排序器使用零样本多语言问题生成模型——一个预训练语言模型——计算输入问题在目标语言下由检索段落条件生成的概率，段落可能与查询语言不同。我们在完全零样本设置下评估该方法，无需任何训练。其主要优势在于可应用于任何稀疏检索方法（如BM-25），无需获取昂贵的标注语料库，因而适用于低资源语言。
原文摘要 · Abstract (English)
Cross-lingual information retrieval (CLIR) ~\cite{shi2021cross, asai2021one, jiang2020cross} for example, can find relevant text in any language such as English(high resource) or Telugu (low resource) even when the query is posed in a different, possibly low-resource, language. In this work, we aim to develop useful CLIR models for this constrained, yet important, setting where we do not require any kind of additional supervision or labelled data for retrieval task and hence can work effectively for low-resource languages. \par We propose a simple and effective re-ranking method for improving passage retrieval in open question answering. The re-ranker re-scores retrieved passages with a zero-shot multilingual question generation model, which is a pre-trained language model, to compute the probability of the input question in the target language conditioned on a retrieved passage, which can be possibly in a different language. We evaluate our method in a completely zero shot setting and doesn't require any training. Thus the main advantage of our method is that our approach can be used to re-rank results obtained by any sparse retrieval methods like BM-25. This eliminates the need for obtaining expensive labelled corpus required for the retrieval tasks and hence can be used for low resource languages.
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