  Multiple critical scenarios (like Wikipedia text generation given English
Infoboxes) need automated generation of descriptive text in low resource (LR)
languages from English fact triples. Previous work has focused on English
fact-to-text (F2T) generation. To the best of our knowledge, there has been no
previous attempt on cross-lingual alignment or generation for LR languages.
Building an effective cross-lingual F2T (XF2T) system requires alignment
between English structured facts and LR sentences. We propose two unsupervised
methods for cross-lingual alignment. We contribute XALIGN, an XF2T dataset with
0.45M pairs across 8 languages, of which 5402 pairs have been manually
annotated. We also train strong baseline XF2T generation models on the XAlign
dataset.
