African languages have far less in-language content available digitally,
making it challenging for question answering systems to satisfy the information
needs of users. Cross-lingual open-retrieval question answering (XOR QA)
systems -- those that retrieve answer content from other languages while
serving people in their native language -- offer a means of filling this gap.
To this end, we create AfriQA, the first cross-lingual QA dataset with a focus
on African languages. AfriQA includes 12,000+ XOR QA examples across 10 African
languages. While previous datasets have focused primarily on languages where
cross-lingual QA augments coverage from the target language, AfriQA focuses on
languages where cross-lingual answer content is the only high-coverage source
of answer content. Because of this, we argue that African languages are one of
the most important and realistic use cases for XOR QA. Our experiments
demonstrate the poor performance of automatic translation and multilingual
retrieval methods. Overall, AfriQA proves challenging for state-of-the-art QA
models. We hope that the dataset enables the development of more equitable QA
technology.

AfriQA 是第一个集中于非洲语言的交叉语言问答数据集，包括了 10 种非洲语言的 12,000 多个交叉语言问答样例。该研究表明自动翻译和多语种检索方法的效果不佳，AfriQA 对最先进的问答模型提出了巨大挑战，并期望该数据集能够促进更公平的 QA 技术的发展。

AfriQA: 面向非洲语言的跨语言开放式检索问答

AfriQA: Cross-lingual Open-Retrieval Question Answering for African  Languages

We describe our two-stage system for the Multilingual Information Access
(MIA) 2022 Shared Task on Cross-Lingual Open-Retrieval Question Answering. The
first stage consists of multilingual passage retrieval with a hybrid dense and
sparse retrieval strategy. The second stage consists of a reader which outputs
the answer from the top passages returned by the first stage. We show the
efficacy of using a multilingual language model with entity representations in
pretraining, sparse retrieval signals to help dense retrieval, and
Fusion-in-Decoder. On the development set, we obtain 43.46 F1 on XOR-TyDi QA
and 21.99 F1 on MKQA, for an average F1 score of 32.73. On the test set, we
obtain 40.93 F1 on XOR-TyDi QA and 22.29 F1 on MKQA, for an average F1 score of
31.61. We improve over the official baseline by over 4 F1 points on both the
development and test sets.

本文介绍了针对 Multilingual Information Access 2022 中 Cross-Lingual Open-Retrieval Question Answering 的任务的一个二阶段系统，包含多语言的检索和回答机制，使用了基于 entity 的多语言语言模型、稀疏信号和 Fusion-in-Decoder 等策略来提高表现。在测试集和开发集中，该系统的平均 F1 分别为 31.61 和 32.73，与官方基准线相比提高了 4 个 F1 分数。

MIA 2022 共享任务提交：利用实体表示、密集稀疏混合和解码器中的融合进行跨语言问答

MIA 2022 Shared Task Submission: Leveraging Entity Representations, Dense-Sparse Hybrids, and Fusion-in-Decoder for Cross-Lingual Question Answering

This paper introduces our proposed system for the MIA Shared Task on
Cross-lingual Open-retrieval Question Answering (COQA). In this challenging
scenario, given an input question the system has to gather evidence documents
from a multilingual pool and generate from them an answer in the language of
the question. We devised several approaches combining different model variants
for three main components: Data Augmentation, Passage Retrieval, and Answer
Generation. For passage retrieval, we evaluated the monolingual BM25 ranker
against the ensemble of re-rankers based on multilingual pretrained language
models (PLMs) and also variants of the shared task baseline, re-training it
from scratch using a recently introduced contrastive loss that maintains a
strong gradient signal throughout training by means of mixed negative samples.
For answer generation, we focused on language- and domain-specialization by
means of continued language model (LM) pretraining of existing multilingual
encoders. Additionally, for both passage retrieval and answer generation, we
augmented the training data provided by the task organizers with automatically
generated question-answer pairs created from Wikipedia passages to mitigate the
issue of data scarcity, particularly for the low-resource languages for which
no training data were provided. Our results show that language- and
domain-specialization as well as data augmentation help, especially for
low-resource languages.

该论文提出了一种用于 COQA 多语言问题解答的系统，该系统使用多种模型变体在数据增强、语段检索和答案生成三个主要组件上进行了研究，并结合语言模型预训练和数据增强等方法有效提高了针对低资源语言的表现。