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Understanding when two pieces of text convey the same information is a goaltouching many subproblems in NLP, including textual entailment andfact-checking. This problem becomes more complex when those two pieces of textare in different languages. Here, we introduce X-PARADE (Cross-lingualParagraph-level Analysis of Divergences and Entailments), the firstcross-lingual dataset of paragraph-level information divergences. Annotatorslabel a paragraph in a target language at the span level and evaluate it withrespect to a corresponding paragraph in a source language, indicating whether agiven piece of information is the same, new, or new but can be inferred. Thislast notion establishes a link with cross-language NLI. Aligned paragraphs aresourced from Wikipedia pages in different languages, reflecting realinformation divergences observed in the wild. Armed with our dataset, weinvestigate a diverse set of approaches for this problem, including classictoken alignment from machine translation, textual entailment methods thatlocalize their decisions, and prompting of large language models. Our resultsshow that these methods vary in their capability to handle inferableinformation, but they all fall short of human performance.

X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs