  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.
