Papers › XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages
XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages
Shivprasad Sagare, Tushar Abhishek, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta, Vasudeva Varma
Multiple business scenarios require an automated generation of descriptive human-readable text from structured input data. Hence, fact-to-text generation systems have been developed for various downstream tasks like generating soccer reports, weather and financial reports, medical reports, person biographies, etc. Unfortunately, previous work on fact-to-text (F2T) generation has focused primarily on English mainly due to the high availability of relevant datasets. Only recently, the problem of cross-lingual fact-to-text (XF2T) was proposed for generation across multiple languages alongwith a dataset, XALIGN for eight languages. However, there has been no rigorous work on the actual XF2T generation problem. We extend XALIGN dataset with annotated data for four more languages: Punjabi, Malayalam, Assamese and Oriya. We conduct an extensive study using popular Transformer-based text generation models on our extended multi-lingual dataset, which we call XALIGNV2. Further, we investigate the performance of different text generation strategies: multiple variations of pretraining, fact-aware embeddings and structure-aware input encoding. Our extensive experiments show that a multi-lingual mT5 model which uses fact-aware embeddings with structure-aware input encoding leads to best results on average across the twelve languages. We make our code, dataset and model publicly available, and hope that this will help advance further research in this critical area.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Data-to-Text Generation | XAlign | Fact-aware embedding with mT5 | BLEU4 | 29.27 | #1 of 6 | Archive leaderboard | report |
| Data-to-Text Generation | XAlign | Fact-aware embedding with mT5 | METEOR | 53.64 | #1 of 6 | Archive leaderboard | report |
| Data-to-Text Generation | XAlign | Bi-lingual mT5 | BLEU4 | 25.88 | #2 of 6 | Archive leaderboard | report |
| Data-to-Text Generation | XAlign | Bi-lingual mT5 | METEOR | 50.91 | #2 of 6 | Archive leaderboard | report |
| Data-to-Text Generation | XAlign | Translate-Output mT5 | BLEU4 | 18.91 | #5 of 6 | Archive leaderboard | report |
| Data-to-Text Generation | XAlign | Translate-Output mT5 | METEOR | 42.83 | #5 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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