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。

跨语言产品问答：12 种语言

xPQA: Cross-Lingual Product Question Answering across 12 Languages

Promptly and accurately answering questions on products is important for
e-commerce applications. Manually answering product questions (e.g. on
community question answering platforms) results in slow response and does not
scale. Recent studies show that product reviews are a good source for
real-time, automatic product question answering (PQA). In the literature, PQA
is formulated as a retrieval problem with the goal to search for the most
relevant reviews to answer a given product question. In this paper, we focus on
the issue of answerability and answer reliability for PQA using reviews. Our
investigation is based on the intuition that many questions may not be
answerable with a finite set of reviews. When a question is not answerable, a
system should return nil answers rather than providing a list of irrelevant
reviews, which can have significant negative impact on user experience.
Moreover, for answerable questions, only the most relevant reviews that answer
the question should be included in the result. We propose a conformal
prediction based framework to improve the reliability of PQA systems, where we
reject unreliable answers so that the returned results are more concise and
accurate at answering the product question, including returning nil answers for
unanswerable questions. Experiments on a widely used Amazon dataset show
encouraging results of our proposed framework. More broadly, our results
demonstrate a novel and effective application of conformal methods to a
retrieval task.

本研究提出了一种基于符合性预测的产品问答系统，可以提高回答的可靠性和可信度，并且包括不可以回答的问题的空答案，实现了对产品问题的快速和准确的回答。