Product Question Answering (PQA) systems are key in e-commerce applications
to provide responses to customers' questions as they shop for products. While
existing work on PQA focuses mainly on English, in practice there is need to
support multiple customer languages while leveraging product information
available in English. To study this practical industrial task, we present xPQA,
a large-scale annotated cross-lingual PQA dataset in 12 languages across 9
branches, and report results in (1) candidate ranking, to select the best
English candidate containing the information to answer a non-English question;
and (2) answer generation, to generate a natural-sounding non-English answer
based on the selected English candidate. We evaluate various approaches
involving machine translation at runtime or offline, leveraging multilingual
pre-trained LMs, and including or excluding xPQA training data. We find that
(1) In-domain data is essential as cross-lingual rankers trained on other
domains perform poorly on the PQA task; (2) Candidate ranking often prefers
runtime-translation approaches while answer generation prefers multilingual
approaches; (3) Translating offline to augment multilingual models helps
candidate ranking mainly on languages with non-Latin scripts; and helps answer
generation mainly on languages with Latin scripts. Still, there remains a
significant performance gap between the English and the cross-lingual test
sets.

该研究致力于针对电子商务应用中需要支持多语言的情况，建立一种跨语言的产品问答 (PQA) 系统，为了实现这一目标，它提出了一个包括 12 种语言在九个领域中的大规模标注跨语言 PQA 数据集，并评估了包括机器翻译在内的多种方法。作者强调，领域内的数据对跨语言排序任务非常重要，而候选的排序通常更喜欢基于运行时翻译的方法，而答案的生成更倾向于使用多语言方法，但是英语和跨语言测试集之间仍存在明显的性能差距。