Papers › XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages
XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages
Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta, Vasudeva Varma
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Data-to-Text Generation | XAlign | mT5 | BLEU4 | 25 | #3 of 6 | Archive leaderboard | report |
| Data-to-Text Generation | XAlign | Vanilla Transformer | BLEU4 | 19.9 | #4 of 6 | Archive leaderboard | report |
| Data-to-Text Generation | XAlign | Graph Attention Network Encoder +Transformer Decoder | BLEU4 | 18.3 | #6 of 6 | Archive leaderboard | report |
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