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。