We present LAReQA, a challenging new benchmark for language-agnostic answer
retrieval from a multilingual candidate pool. Unlike previous cross-lingual
tasks, LAReQA tests for "strong" cross-lingual alignment, requiring
semantically related cross-language pairs to be closer in representation space
than unrelated same-language pairs. Building on multilingual BERT (mBERT), we
study different strategies for achieving strong alignment. We find that
augmenting training data via machine translation is effective, and improves
significantly over using mBERT out-of-the-box. Interestingly, the embedding
baseline that performs the best on LAReQA falls short of competing baselines on
zero-shot variants of our task that only target "weak" alignment. This finding
underscores our claim that languageagnostic retrieval is a substantively new
kind of cross-lingual evaluation.

介绍了 LAReQA - 一项新的挑战性基准测试，可从多语言候选池中进行语言无关的答案检索。在基于多语言 BERT 的基础上，研究了实现强交叉语言对齐的不同策略，发现通过机器翻译增加训练数据是有效的，同时，该基线在 LAReQA 上表现最好，但在仅瞄准 “弱” 对齐的任务中却不及竞争基线，这突显了语言无关检索是一种实质性新型跨语言评估。