Understanding when two pieces of text convey the same information is a goal
touching many subproblems in NLP, including textual entailment and
fact-checking. This problem becomes more complex when those two pieces of text
are in different languages. Here, we introduce X-PARADE (Cross-lingual
Paragraph-level Analysis of Divergences and Entailments), the first
cross-lingual dataset of paragraph-level information divergences. Annotators
label a paragraph in a target language at the span level and evaluate it with
respect to a corresponding paragraph in a source language, indicating whether a
given piece of information is the same, new, or new but can be inferred. This
last notion establishes a link with cross-language NLI. Aligned paragraphs are
sourced from Wikipedia pages in different languages, reflecting real
information divergences observed in the wild. Armed with our dataset, we
investigate a diverse set of approaches for this problem, including classic
token alignment from machine translation, textual entailment methods that
localize their decisions, and prompting of large language models. Our results
show that these methods vary in their capability to handle inferable
information, but they all fall short of human performance.

X-PARADE 是第一个跨语言段落级别信息差异分析数据集，研究了不同语言之间的信息一致性问题，并探索了跨语言 NLI 的可能方法，结果表明各种方法都无法达到人类的表现水平。